
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import re

warnings.filterwarnings('ignore')
%matplotlib inline


pd.set_option("display.max_columns",250)
pd.set_option("display.max_rows",250)
pd.set_option("display.width",2000)


previous_application=pd.read_csv('E:/VCET/Trainity Intern/Project 6 Bank Loan Case Study/previous_application.csv')
    application_data=pd.read_csv('E:/VCET/Trainity Intern/Project 6 Bank Loan Case Study/application_data.csv')


previous_application.shape

(1670214, 37)


application_data.shape

(307511, 122)


previous_application.info(verbose=True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
 #   Column                       Non-Null Count    Dtype  
---  ------                       --------------    -----  
 0   SK_ID_PREV                   1670214 non-null  int64  
 1   SK_ID_CURR                   1670214 non-null  int64  
 2   NAME_CONTRACT_TYPE           1670214 non-null  object 
 3   AMT_ANNUITY                  1297979 non-null  float64
 4   AMT_APPLICATION              1670214 non-null  float64
 5   AMT_CREDIT                   1670213 non-null  float64
 6   AMT_DOWN_PAYMENT             774370 non-null   float64
 7   AMT_GOODS_PRICE              1284699 non-null  float64
 8   WEEKDAY_APPR_PROCESS_START   1670214 non-null  object 
 9   HOUR_APPR_PROCESS_START      1670214 non-null  int64  
 10  FLAG_LAST_APPL_PER_CONTRACT  1670214 non-null  object 
 11  NFLAG_LAST_APPL_IN_DAY       1670214 non-null  int64  
 12  RATE_DOWN_PAYMENT            774370 non-null   float64
 13  RATE_INTEREST_PRIMARY        5951 non-null     float64
 14  RATE_INTEREST_PRIVILEGED     5951 non-null     float64
 15  NAME_CASH_LOAN_PURPOSE       1670214 non-null  object 
 16  NAME_CONTRACT_STATUS         1670214 non-null  object 
 17  DAYS_DECISION                1670214 non-null  int64  
 18  NAME_PAYMENT_TYPE            1670214 non-null  object 
 19  CODE_REJECT_REASON           1670214 non-null  object 
 20  NAME_TYPE_SUITE              849809 non-null   object 
 21  NAME_CLIENT_TYPE             1670214 non-null  object 
 22  NAME_GOODS_CATEGORY          1670214 non-null  object 
 23  NAME_PORTFOLIO               1670214 non-null  object 
 24  NAME_PRODUCT_TYPE            1670214 non-null  object 
 25  CHANNEL_TYPE                 1670214 non-null  object 
 26  SELLERPLACE_AREA             1670214 non-null  int64  
 27  NAME_SELLER_INDUSTRY         1670214 non-null  object 
 28  CNT_PAYMENT                  1297984 non-null  float64
 29  NAME_YIELD_GROUP             1670214 non-null  object 
 30  PRODUCT_COMBINATION          1669868 non-null  object 
 31  DAYS_FIRST_DRAWING           997149 non-null   float64
 32  DAYS_FIRST_DUE               997149 non-null   float64
 33  DAYS_LAST_DUE_1ST_VERSION    997149 non-null   float64
 34  DAYS_LAST_DUE                997149 non-null   float64
 35  DAYS_TERMINATION             997149 non-null   float64
 36  NFLAG_INSURED_ON_APPROVAL    997149 non-null   float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB


application_data.info(verbose=True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Data columns (total 122 columns):
 #    Column                        Dtype  
---   ------                        -----  
 0    SK_ID_CURR                    int64  
 1    TARGET                        int64  
 2    NAME_CONTRACT_TYPE            object 
 3    CODE_GENDER                   object 
 4    FLAG_OWN_CAR                  object 
 5    FLAG_OWN_REALTY               object 
 6    CNT_CHILDREN                  int64  
 7    AMT_INCOME_TOTAL              float64
 8    AMT_CREDIT                    float64
 9    AMT_ANNUITY                   float64
 10   AMT_GOODS_PRICE               float64
 11   NAME_TYPE_SUITE               object 
 12   NAME_INCOME_TYPE              object 
 13   NAME_EDUCATION_TYPE           object 
 14   NAME_FAMILY_STATUS            object 
 15   NAME_HOUSING_TYPE             object 
 16   REGION_POPULATION_RELATIVE    float64
 17   DAYS_BIRTH                    int64  
 18   DAYS_EMPLOYED                 int64  
 19   DAYS_REGISTRATION             float64
 20   DAYS_ID_PUBLISH               int64  
 21   OWN_CAR_AGE                   float64
 22   FLAG_MOBIL                    int64  
 23   FLAG_EMP_PHONE                int64  
 24   FLAG_WORK_PHONE               int64  
 25   FLAG_CONT_MOBILE              int64  
 26   FLAG_PHONE                    int64  
 27   FLAG_EMAIL                    int64  
 28   OCCUPATION_TYPE               object 
 29   CNT_FAM_MEMBERS               float64
 30   REGION_RATING_CLIENT          int64  
 31   REGION_RATING_CLIENT_W_CITY   int64  
 32   WEEKDAY_APPR_PROCESS_START    object 
 33   HOUR_APPR_PROCESS_START       int64  
 34   REG_REGION_NOT_LIVE_REGION    int64  
 35   REG_REGION_NOT_WORK_REGION    int64  
 36   LIVE_REGION_NOT_WORK_REGION   int64  
 37   REG_CITY_NOT_LIVE_CITY        int64  
 38   REG_CITY_NOT_WORK_CITY        int64  
 39   LIVE_CITY_NOT_WORK_CITY       int64  
 40   ORGANIZATION_TYPE             object 
 41   EXT_SOURCE_1                  float64
 42   EXT_SOURCE_2                  float64
 43   EXT_SOURCE_3                  float64
 44   APARTMENTS_AVG                float64
 45   BASEMENTAREA_AVG              float64
 46   YEARS_BEGINEXPLUATATION_AVG   float64
 47   YEARS_BUILD_AVG               float64
 48   COMMONAREA_AVG                float64
 49   ELEVATORS_AVG                 float64
 50   ENTRANCES_AVG                 float64
 51   FLOORSMAX_AVG                 float64
 52   FLOORSMIN_AVG                 float64
 53   LANDAREA_AVG                  float64
 54   LIVINGAPARTMENTS_AVG          float64
 55   LIVINGAREA_AVG                float64
 56   NONLIVINGAPARTMENTS_AVG       float64
 57   NONLIVINGAREA_AVG             float64
 58   APARTMENTS_MODE               float64
 59   BASEMENTAREA_MODE             float64
 60   YEARS_BEGINEXPLUATATION_MODE  float64
 61   YEARS_BUILD_MODE              float64
 62   COMMONAREA_MODE               float64
 63   ELEVATORS_MODE                float64
 64   ENTRANCES_MODE                float64
 65   FLOORSMAX_MODE                float64
 66   FLOORSMIN_MODE                float64
 67   LANDAREA_MODE                 float64
 68   LIVINGAPARTMENTS_MODE         float64
 69   LIVINGAREA_MODE               float64
 70   NONLIVINGAPARTMENTS_MODE      float64
 71   NONLIVINGAREA_MODE            float64
 72   APARTMENTS_MEDI               float64
 73   BASEMENTAREA_MEDI             float64
 74   YEARS_BEGINEXPLUATATION_MEDI  float64
 75   YEARS_BUILD_MEDI              float64
 76   COMMONAREA_MEDI               float64
 77   ELEVATORS_MEDI                float64
 78   ENTRANCES_MEDI                float64
 79   FLOORSMAX_MEDI                float64
 80   FLOORSMIN_MEDI                float64
 81   LANDAREA_MEDI                 float64
 82   LIVINGAPARTMENTS_MEDI         float64
 83   LIVINGAREA_MEDI               float64
 84   NONLIVINGAPARTMENTS_MEDI      float64
 85   NONLIVINGAREA_MEDI            float64
 86   FONDKAPREMONT_MODE            object 
 87   HOUSETYPE_MODE                object 
 88   TOTALAREA_MODE                float64
 89   WALLSMATERIAL_MODE            object 
 90   EMERGENCYSTATE_MODE           object 
 91   OBS_30_CNT_SOCIAL_CIRCLE      float64
 92   DEF_30_CNT_SOCIAL_CIRCLE      float64
 93   OBS_60_CNT_SOCIAL_CIRCLE      float64
 94   DEF_60_CNT_SOCIAL_CIRCLE      float64
 95   DAYS_LAST_PHONE_CHANGE        float64
 96   FLAG_DOCUMENT_2               int64  
 97   FLAG_DOCUMENT_3               int64  
 98   FLAG_DOCUMENT_4               int64  
 99   FLAG_DOCUMENT_5               int64  
 100  FLAG_DOCUMENT_6               int64  
 101  FLAG_DOCUMENT_7               int64  
 102  FLAG_DOCUMENT_8               int64  
 103  FLAG_DOCUMENT_9               int64  
 104  FLAG_DOCUMENT_10              int64  
 105  FLAG_DOCUMENT_11              int64  
 106  FLAG_DOCUMENT_12              int64  
 107  FLAG_DOCUMENT_13              int64  
 108  FLAG_DOCUMENT_14              int64  
 109  FLAG_DOCUMENT_15              int64  
 110  FLAG_DOCUMENT_16              int64  
 111  FLAG_DOCUMENT_17              int64  
 112  FLAG_DOCUMENT_18              int64  
 113  FLAG_DOCUMENT_19              int64  
 114  FLAG_DOCUMENT_20              int64  
 115  FLAG_DOCUMENT_21              int64  
 116  AMT_REQ_CREDIT_BUREAU_HOUR    float64
 117  AMT_REQ_CREDIT_BUREAU_DAY     float64
 118  AMT_REQ_CREDIT_BUREAU_WEEK    float64
 119  AMT_REQ_CREDIT_BUREAU_MON     float64
 120  AMT_REQ_CREDIT_BUREAU_QRT     float64
 121  AMT_REQ_CREDIT_BUREAU_YEAR    float64
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB


previous_application.describe()


application_data.describe()


previous_application.agg(['count','size','nunique'])


application_data.agg(['count','size','nunique'])


round(previous_application.isnull().sum()/previous_application.shape[0]*100,2).sort_values(ascending=False)

RATE_INTEREST_PRIVILEGED       99.64
RATE_INTEREST_PRIMARY          99.64
RATE_DOWN_PAYMENT              53.64
AMT_DOWN_PAYMENT               53.64
NAME_TYPE_SUITE                49.12
NFLAG_INSURED_ON_APPROVAL      40.30
DAYS_FIRST_DRAWING             40.30
DAYS_FIRST_DUE                 40.30
DAYS_LAST_DUE_1ST_VERSION      40.30
DAYS_LAST_DUE                  40.30
DAYS_TERMINATION               40.30
AMT_GOODS_PRICE                23.08
AMT_ANNUITY                    22.29
CNT_PAYMENT                    22.29
PRODUCT_COMBINATION             0.02
CHANNEL_TYPE                    0.00
NAME_PRODUCT_TYPE               0.00
NAME_YIELD_GROUP                0.00
SELLERPLACE_AREA                0.00
NAME_SELLER_INDUSTRY            0.00
NAME_GOODS_CATEGORY             0.00
NAME_PORTFOLIO                  0.00
SK_ID_PREV                      0.00
NAME_CLIENT_TYPE                0.00
CODE_REJECT_REASON              0.00
SK_ID_CURR                      0.00
DAYS_DECISION                   0.00
NAME_CONTRACT_STATUS            0.00
NAME_CASH_LOAN_PURPOSE          0.00
NFLAG_LAST_APPL_IN_DAY          0.00
FLAG_LAST_APPL_PER_CONTRACT     0.00
HOUR_APPR_PROCESS_START         0.00
WEEKDAY_APPR_PROCESS_START      0.00
AMT_CREDIT                      0.00
AMT_APPLICATION                 0.00
NAME_CONTRACT_TYPE              0.00
NAME_PAYMENT_TYPE               0.00
dtype: float64


deleted_column.shape

(307511, 49)


deleted_column.columns

Index(['OWN_CAR_AGE', 'EXT_SOURCE_1', 'APARTMENTS_AVG', 'BASEMENTAREA_AVG', 'YEARS_BEGINEXPLUATATION_AVG', 'YEARS_BUILD_AVG', 'COMMONAREA_AVG', 'ELEVATORS_AVG', 'ENTRANCES_AVG', 'FLOORSMAX_AVG', 'FLOORSMIN_AVG', 'LANDAREA_AVG', 'LIVINGAPARTMENTS_AVG', 'LIVINGAREA_AVG', 'NONLIVINGAPARTMENTS_AVG', 'NONLIVINGAREA_AVG', 'APARTMENTS_MODE', 'BASEMENTAREA_MODE', 'YEARS_BEGINEXPLUATATION_MODE', 'YEARS_BUILD_MODE', 'COMMONAREA_MODE', 'ELEVATORS_MODE', 'ENTRANCES_MODE', 'FLOORSMAX_MODE', 'FLOORSMIN_MODE', 'LANDAREA_MODE', 'LIVINGAPARTMENTS_MODE', 'LIVINGAREA_MODE', 'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAREA_MODE', 'APARTMENTS_MEDI', 'BASEMENTAREA_MEDI', 'YEARS_BEGINEXPLUATATION_MEDI', 'YEARS_BUILD_MEDI', 'COMMONAREA_MEDI', 'ELEVATORS_MEDI', 'ENTRANCES_MEDI', 'FLOORSMAX_MEDI', 'FLOORSMIN_MEDI', 'LANDAREA_MEDI', 'LIVINGAPARTMENTS_MEDI', 'LIVINGAREA_MEDI', 'NONLIVINGAPARTMENTS_MEDI', 'NONLIVINGAREA_MEDI', 'FONDKAPREMONT_MODE', 'HOUSETYPE_MODE', 'TOTALAREA_MODE', 'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE'], dtype='object')


round(application_data_up.isnull().sum()/application_data_up.shape[0]*100,2).sort_values(ascending=False)

OCCUPATION_TYPE                31.35
EXT_SOURCE_3                   19.83
AMT_REQ_CREDIT_BUREAU_YEAR     13.50
AMT_REQ_CREDIT_BUREAU_QRT      13.50
AMT_REQ_CREDIT_BUREAU_MON      13.50
AMT_REQ_CREDIT_BUREAU_WEEK     13.50
AMT_REQ_CREDIT_BUREAU_DAY      13.50
AMT_REQ_CREDIT_BUREAU_HOUR     13.50
NAME_TYPE_SUITE                 0.42
OBS_30_CNT_SOCIAL_CIRCLE        0.33
DEF_30_CNT_SOCIAL_CIRCLE        0.33
OBS_60_CNT_SOCIAL_CIRCLE        0.33
DEF_60_CNT_SOCIAL_CIRCLE        0.33
EXT_SOURCE_2                    0.21
AMT_GOODS_PRICE                 0.09
FLAG_DOCUMENT_7                 0.00
DAYS_LAST_PHONE_CHANGE          0.00
FLAG_DOCUMENT_2                 0.00
FLAG_DOCUMENT_3                 0.00
FLAG_DOCUMENT_4                 0.00
FLAG_DOCUMENT_5                 0.00
FLAG_DOCUMENT_6                 0.00
FLAG_DOCUMENT_11                0.00
FLAG_DOCUMENT_8                 0.00
FLAG_DOCUMENT_9                 0.00
FLAG_DOCUMENT_10                0.00
LIVE_CITY_NOT_WORK_CITY         0.00
FLAG_DOCUMENT_12                0.00
FLAG_DOCUMENT_13                0.00
FLAG_DOCUMENT_14                0.00
FLAG_DOCUMENT_15                0.00
FLAG_DOCUMENT_16                0.00
FLAG_DOCUMENT_17                0.00
FLAG_DOCUMENT_18                0.00
FLAG_DOCUMENT_19                0.00
FLAG_DOCUMENT_20                0.00
FLAG_DOCUMENT_21                0.00
ORGANIZATION_TYPE               0.00
SK_ID_CURR                      0.00
REG_CITY_NOT_WORK_CITY          0.00
AMT_ANNUITY                     0.00
DAYS_BIRTH                      0.00
REGION_POPULATION_RELATIVE      0.00
NAME_HOUSING_TYPE               0.00
NAME_FAMILY_STATUS              0.00
NAME_EDUCATION_TYPE             0.00
NAME_INCOME_TYPE                0.00
AMT_CREDIT                      0.00
TARGET                          0.00
AMT_INCOME_TOTAL                0.00
CNT_CHILDREN                    0.00
FLAG_OWN_REALTY                 0.00
FLAG_OWN_CAR                    0.00
CODE_GENDER                     0.00
NAME_CONTRACT_TYPE              0.00
DAYS_EMPLOYED                   0.00
DAYS_REGISTRATION               0.00
DAYS_ID_PUBLISH                 0.00
FLAG_MOBIL                      0.00
FLAG_EMP_PHONE                  0.00
FLAG_WORK_PHONE                 0.00
FLAG_CONT_MOBILE                0.00
FLAG_PHONE                      0.00
FLAG_EMAIL                      0.00
CNT_FAM_MEMBERS                 0.00
REGION_RATING_CLIENT            0.00
REGION_RATING_CLIENT_W_CITY     0.00
WEEKDAY_APPR_PROCESS_START      0.00
HOUR_APPR_PROCESS_START         0.00
REG_REGION_NOT_LIVE_REGION      0.00
REG_REGION_NOT_WORK_REGION      0.00
LIVE_REGION_NOT_WORK_REGION     0.00
REG_CITY_NOT_LIVE_CITY          0.00
dtype: float64


application_data_up["OCCUPATION_TYPE"].unique()

array(['Laborers', 'Core staff', 'Accountants', 'Managers', nan,
       'Drivers', 'Sales staff', 'Cleaning staff', 'Cooking staff',
       'Private service staff', 'Medicine staff', 'Security staff',
       'High skill tech staff', 'Waiters/barmen staff',
       'Low-skill Laborers', 'Realty agents', 'Secretaries', 'IT staff',
       'HR staff'], dtype=object)


application_data_up["OCCUPATION_TYPE"].value_counts()

Laborers                 55186
Sales staff              32102
Core staff               27570
Managers                 21371
Drivers                  18603
High skill tech staff    11380
Accountants               9813
Medicine staff            8537
Security staff            6721
Cooking staff             5946
Cleaning staff            4653
Private service staff     2652
Low-skill Laborers        2093
Waiters/barmen staff      1348
Secretaries               1305
Realty agents              751
HR staff                   563
IT staff                   526
Name: OCCUPATION_TYPE, dtype: int64


application_data_up["OCCUPATION_TYPE"].isnull().sum()

96391


application_data_up["OCCUPATION_TYPE"].replace(np.NaN, "unknown", inplace=True)


application_data_up["OCCUPATION_TYPE"].isnull().sum()

0


application_data_up["OCCUPATION_TYPE"].value_counts()

unknown                  96391
Laborers                 55186
Sales staff              32102
Core staff               27570
Managers                 21371
Drivers                  18603
High skill tech staff    11380
Accountants               9813
Medicine staff            8537
Security staff            6721
Cooking staff             5946
Cleaning staff            4653
Private service staff     2652
Low-skill Laborers        2093
Waiters/barmen staff      1348
Secretaries               1305
Realty agents              751
HR staff                   563
IT staff                   526
Name: OCCUPATION_TYPE, dtype: int64


round(application_data_up.isnull().sum()/application_data_up.shape[0]*100,2).sort_values(ascending=False)

EXT_SOURCE_3                   19.83
AMT_REQ_CREDIT_BUREAU_YEAR     13.50
AMT_REQ_CREDIT_BUREAU_QRT      13.50
AMT_REQ_CREDIT_BUREAU_MON      13.50
AMT_REQ_CREDIT_BUREAU_WEEK     13.50
AMT_REQ_CREDIT_BUREAU_DAY      13.50
AMT_REQ_CREDIT_BUREAU_HOUR     13.50
NAME_TYPE_SUITE                 0.42
OBS_30_CNT_SOCIAL_CIRCLE        0.33
DEF_30_CNT_SOCIAL_CIRCLE        0.33
OBS_60_CNT_SOCIAL_CIRCLE        0.33
DEF_60_CNT_SOCIAL_CIRCLE        0.33
EXT_SOURCE_2                    0.21
AMT_GOODS_PRICE                 0.09
FLAG_DOCUMENT_8                 0.00
DAYS_LAST_PHONE_CHANGE          0.00
FLAG_DOCUMENT_2                 0.00
FLAG_DOCUMENT_3                 0.00
FLAG_DOCUMENT_4                 0.00
FLAG_DOCUMENT_5                 0.00
FLAG_DOCUMENT_6                 0.00
FLAG_DOCUMENT_7                 0.00
FLAG_DOCUMENT_12                0.00
FLAG_DOCUMENT_9                 0.00
FLAG_DOCUMENT_10                0.00
FLAG_DOCUMENT_11                0.00
LIVE_CITY_NOT_WORK_CITY         0.00
FLAG_DOCUMENT_13                0.00
FLAG_DOCUMENT_14                0.00
FLAG_DOCUMENT_15                0.00
FLAG_DOCUMENT_16                0.00
FLAG_DOCUMENT_17                0.00
FLAG_DOCUMENT_18                0.00
FLAG_DOCUMENT_19                0.00
FLAG_DOCUMENT_20                0.00
FLAG_DOCUMENT_21                0.00
ORGANIZATION_TYPE               0.00
SK_ID_CURR                      0.00
REG_CITY_NOT_WORK_CITY          0.00
AMT_ANNUITY                     0.00
DAYS_BIRTH                      0.00
REGION_POPULATION_RELATIVE      0.00
NAME_HOUSING_TYPE               0.00
NAME_FAMILY_STATUS              0.00
NAME_EDUCATION_TYPE             0.00
NAME_INCOME_TYPE                0.00
AMT_CREDIT                      0.00
DAYS_REGISTRATION               0.00
AMT_INCOME_TOTAL                0.00
CNT_CHILDREN                    0.00
FLAG_OWN_REALTY                 0.00
FLAG_OWN_CAR                    0.00
CODE_GENDER                     0.00
NAME_CONTRACT_TYPE              0.00
DAYS_EMPLOYED                   0.00
DAYS_ID_PUBLISH                 0.00
TARGET                          0.00
REGION_RATING_CLIENT            0.00
LIVE_REGION_NOT_WORK_REGION     0.00
REG_REGION_NOT_WORK_REGION      0.00
REG_REGION_NOT_LIVE_REGION      0.00
HOUR_APPR_PROCESS_START         0.00
WEEKDAY_APPR_PROCESS_START      0.00
REGION_RATING_CLIENT_W_CITY     0.00
CNT_FAM_MEMBERS                 0.00
FLAG_MOBIL                      0.00
OCCUPATION_TYPE                 0.00
FLAG_EMAIL                      0.00
FLAG_PHONE                      0.00
FLAG_CONT_MOBILE                0.00
FLAG_WORK_PHONE                 0.00
FLAG_EMP_PHONE                  0.00
REG_CITY_NOT_LIVE_CITY          0.00
dtype: float64


not_required=['FLAG_DOCUMENT_21','FLAG_DOCUMENT_20','FLAG_DOCUMENT_19','FLAG_DOCUMENT_18','FLAG_DOCUMENT_17','FLAG_DOCUMENT_16',
             'FLAG_DOCUMENT_15','FLAG_DOCUMENT_14','FLAG_DOCUMENT_13','FLAG_DOCUMENT_12','FLAG_DOCUMENT_11',
             'FLAG_DOCUMENT_10','FLAG_DOCUMENT_9','FLAG_DOCUMENT_8','FLAG_DOCUMENT_7','FLAG_DOCUMENT_6','FLAG_DOCUMENT_5',
             'FLAG_DOCUMENT_4','FLAG_DOCUMENT_3','FLAG_DOCUMENT_2','OBS_30_CNT_SOCIAL_CIRCLE','DEF_30_CNT_SOCIAL_CIRCLE',
             'OBS_60_CNT_SOCIAL_CIRCLE','DEF_60_CNT_SOCIAL_CIRCLE','AMT_REQ_CREDIT_BUREAU_YEAR','AMT_REQ_CREDIT_BUREAU_MON',
             'AMT_REQ_CREDIT_BUREAU_WEEK','AMT_REQ_CREDIT_BUREAU_DAY','AMT_REQ_CREDIT_BUREAU_HOUR','AMT_REQ_CREDIT_BUREAU_QRT']


application_data_up.drop(labels=not_required,axis=1,inplace=True)


application_data_up.shape

(307511, 43)


round(application_data_up.isnull().sum()/application_data_up.shape[0]*100,2).sort_values(ascending=False)

EXT_SOURCE_3                   19.83
NAME_TYPE_SUITE                 0.42
EXT_SOURCE_2                    0.21
AMT_GOODS_PRICE                 0.09
HOUR_APPR_PROCESS_START         0.00
FLAG_PHONE                      0.00
FLAG_EMAIL                      0.00
OCCUPATION_TYPE                 0.00
CNT_FAM_MEMBERS                 0.00
REGION_RATING_CLIENT            0.00
REGION_RATING_CLIENT_W_CITY     0.00
WEEKDAY_APPR_PROCESS_START      0.00
SK_ID_CURR                      0.00
REG_REGION_NOT_LIVE_REGION      0.00
FLAG_WORK_PHONE                 0.00
REG_REGION_NOT_WORK_REGION      0.00
LIVE_REGION_NOT_WORK_REGION     0.00
REG_CITY_NOT_LIVE_CITY          0.00
REG_CITY_NOT_WORK_CITY          0.00
LIVE_CITY_NOT_WORK_CITY         0.00
ORGANIZATION_TYPE               0.00
FLAG_CONT_MOBILE                0.00
FLAG_MOBIL                      0.00
FLAG_EMP_PHONE                  0.00
TARGET                          0.00
NAME_CONTRACT_TYPE              0.00
CODE_GENDER                     0.00
FLAG_OWN_CAR                    0.00
FLAG_OWN_REALTY                 0.00
CNT_CHILDREN                    0.00
AMT_INCOME_TOTAL                0.00
AMT_CREDIT                      0.00
AMT_ANNUITY                     0.00
NAME_INCOME_TYPE                0.00
NAME_EDUCATION_TYPE             0.00
NAME_FAMILY_STATUS              0.00
NAME_HOUSING_TYPE               0.00
REGION_POPULATION_RELATIVE      0.00
DAYS_BIRTH                      0.00
DAYS_EMPLOYED                   0.00
DAYS_REGISTRATION               0.00
DAYS_ID_PUBLISH                 0.00
DAYS_LAST_PHONE_CHANGE          0.00
dtype: float64


plt.figure(figsize=[30,10])
sns.barplot(x=application_data_up.OCCUPATION_TYPE.value_counts().index,
            y=application_data_up.OCCUPATION_TYPE.value_counts().values).set_title("OCCUPATION TYPE COUNTS",fontsize=40, color='blue',pad=30)
plt.xlabel('OCCUPATION TYPE', fontsize=40, color='blue')
plt.xticks(rotation=45)
plt.show()


plt.figure(figsize=[20,15])

sns.set_style('darkgrid')

plt.subplot(2,2,1)
sns.boxplot(application_data_up['EXT_SOURCE_2']).set_title("EXT_SOURCE_2",fontsize=30,color='blue',pad=30)

plt.subplot(2,2,2)
sns.boxplot(application_data_up['EXT_SOURCE_3']).set_title("EXT_SOURCE_3",fontsize=30,color='blue',pad=30)

plt.subplot(2,2,3)
sns.distplot(application_data_up['EXT_SOURCE_2'],color='b')

plt.subplot(2,2,4)
sns.distplot(application_data_up['EXT_SOURCE_3'],color='b')

plt.show()


#REPLACING THE MISSING VALUES WITH ITS CORRESPONDING MEDIAN
for column in ['EXT_SOURCE_2','EXT_SOURCE_3']:
    application_data_up[column].fillna(application_data_up[column].median(), inplace=True)


# CHECKING THE CORRELATION BETWEEN THE LOAN AMOUNT DEMANDED VS THE GOOD'S PRICE

sns.scatterplot(x=application_data_up['AMT_CREDIT'], y=application_data_up['AMT_GOODS_PRICE'], data=application_data_up)
plt.title("Correlation between the loan amount and the price of goods for which loan was given\n",
         fontdict={'fontsize':35,'fontweight':10,'color':'Blue'})
plt.xlabel("Loan Amount",fontdict={'fontsize':35,'fontweight':10,'color':'Black'})
plt.ylabel("Price of Goods",fontdict={'fontsize':35,'fontweight':10,'color':'Black'})
plt.show()


# Imputing the above mentioned logic 

application_data_up['AMT_GOODS_PRICE']=np.where(application_data_up['AMT_GOODS_PRICE'].isnull()==True,
                                      application_data_up['AMT_CREDIT'],application_data_up['AMT_GOODS_PRICE'])


application_data_up['AMT_GOODS_PRICE'].isnull().sum()

0


import plotly.express as px
import plotly.graph_objs as go
import plotly.figure_factory as ff


#Percentage of each category

go.Figure(data=[go.Pie(labels=application_data_up.NAME_TYPE_SUITE.value_counts().index,
                       values=application_data_up.NAME_TYPE_SUITE.value_counts().values, hole=.5, title="NAME_TYPE_VALUE COUNTS",
                       pull=[0,0.1,0.1,0.1,0.1,0.1,0.1])])


application_data_up['NAME_TYPE_SUITE'].isnull().sum()/application_data_up.shape[0]*100

0.42014757195677555


application_data_up['NAME_TYPE_SUITE'].isnull().sum()

1292


application_data_up['NAME_TYPE_SUITE'].mode()

0    Unaccompanied
Name: NAME_TYPE_SUITE, dtype: object


#Replacing missing values with MODE

application_data_up['NAME_TYPE_SUITE'].fillna(application_data_up['NAME_TYPE_SUITE'].mode()[0],inplace=True)


round(application_data_up.isnull().sum()/application_data_up.shape[0]*100,4).sort_values(ascending=False)

AMT_ANNUITY                    0.0039
CNT_FAM_MEMBERS                0.0007
DAYS_LAST_PHONE_CHANGE         0.0003
HOUR_APPR_PROCESS_START        0.0000
FLAG_CONT_MOBILE               0.0000
FLAG_PHONE                     0.0000
FLAG_EMAIL                     0.0000
OCCUPATION_TYPE                0.0000
REGION_RATING_CLIENT           0.0000
REGION_RATING_CLIENT_W_CITY    0.0000
WEEKDAY_APPR_PROCESS_START     0.0000
REG_REGION_NOT_LIVE_REGION     0.0000
FLAG_EMP_PHONE                 0.0000
REG_REGION_NOT_WORK_REGION     0.0000
LIVE_REGION_NOT_WORK_REGION    0.0000
REG_CITY_NOT_LIVE_CITY         0.0000
REG_CITY_NOT_WORK_CITY         0.0000
LIVE_CITY_NOT_WORK_CITY        0.0000
ORGANIZATION_TYPE              0.0000
EXT_SOURCE_2                   0.0000
EXT_SOURCE_3                   0.0000
FLAG_WORK_PHONE                0.0000
SK_ID_CURR                     0.0000
TARGET                         0.0000
DAYS_ID_PUBLISH                0.0000
NAME_CONTRACT_TYPE             0.0000
CODE_GENDER                    0.0000
FLAG_OWN_CAR                   0.0000
FLAG_OWN_REALTY                0.0000
CNT_CHILDREN                   0.0000
AMT_INCOME_TOTAL               0.0000
AMT_CREDIT                     0.0000
AMT_GOODS_PRICE                0.0000
NAME_TYPE_SUITE                0.0000
NAME_INCOME_TYPE               0.0000
NAME_EDUCATION_TYPE            0.0000
NAME_FAMILY_STATUS             0.0000
NAME_HOUSING_TYPE              0.0000
REGION_POPULATION_RELATIVE     0.0000
DAYS_BIRTH                     0.0000
DAYS_EMPLOYED                  0.0000
DAYS_REGISTRATION              0.0000
FLAG_MOBIL                     0.0000
dtype: float64


#REMAINING COLUMNS WITH NEGLIGIBLE NULL VALUES (LESS THAN 1%)

null_col=['CNT_FAM_MEMBERS','AMT_ANNUITY', 'DAYS_LAST_PHONE_CHANGE']
for column in null_col:
    application_data_up[column].fillna(application_data_up[column].median(), inplace=True)


round(application_data_up.isnull().sum()/application_data_up.shape[0]*100,4).sort_values(ascending=False)

SK_ID_CURR                     0.0
HOUR_APPR_PROCESS_START        0.0
FLAG_CONT_MOBILE               0.0
FLAG_PHONE                     0.0
FLAG_EMAIL                     0.0
OCCUPATION_TYPE                0.0
CNT_FAM_MEMBERS                0.0
REGION_RATING_CLIENT           0.0
REGION_RATING_CLIENT_W_CITY    0.0
WEEKDAY_APPR_PROCESS_START     0.0
REG_REGION_NOT_LIVE_REGION     0.0
FLAG_EMP_PHONE                 0.0
REG_REGION_NOT_WORK_REGION     0.0
LIVE_REGION_NOT_WORK_REGION    0.0
REG_CITY_NOT_LIVE_CITY         0.0
REG_CITY_NOT_WORK_CITY         0.0
LIVE_CITY_NOT_WORK_CITY        0.0
ORGANIZATION_TYPE              0.0
EXT_SOURCE_2                   0.0
EXT_SOURCE_3                   0.0
FLAG_WORK_PHONE                0.0
FLAG_MOBIL                     0.0
TARGET                         0.0
AMT_GOODS_PRICE                0.0
NAME_CONTRACT_TYPE             0.0
CODE_GENDER                    0.0
FLAG_OWN_CAR                   0.0
FLAG_OWN_REALTY                0.0
CNT_CHILDREN                   0.0
AMT_INCOME_TOTAL               0.0
AMT_CREDIT                     0.0
AMT_ANNUITY                    0.0
NAME_TYPE_SUITE                0.0
DAYS_ID_PUBLISH                0.0
NAME_INCOME_TYPE               0.0
NAME_EDUCATION_TYPE            0.0
NAME_FAMILY_STATUS             0.0
NAME_HOUSING_TYPE              0.0
REGION_POPULATION_RELATIVE     0.0
DAYS_BIRTH                     0.0
DAYS_EMPLOYED                  0.0
DAYS_REGISTRATION              0.0
DAYS_LAST_PHONE_CHANGE         0.0
dtype: float64


application_data_up.info(verbose=True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Data columns (total 43 columns):
 #   Column                       Non-Null Count   Dtype  
---  ------                       --------------   -----  
 0   SK_ID_CURR                   307511 non-null  int64  
 1   TARGET                       307511 non-null  int64  
 2   NAME_CONTRACT_TYPE           307511 non-null  object 
 3   CODE_GENDER                  307511 non-null  object 
 4   FLAG_OWN_CAR                 307511 non-null  object 
 5   FLAG_OWN_REALTY              307511 non-null  object 
 6   CNT_CHILDREN                 307511 non-null  int64  
 7   AMT_INCOME_TOTAL             307511 non-null  float64
 8   AMT_CREDIT                   307511 non-null  float64
 9   AMT_ANNUITY                  307511 non-null  float64
 10  AMT_GOODS_PRICE              307511 non-null  float64
 11  NAME_TYPE_SUITE              307511 non-null  object 
 12  NAME_INCOME_TYPE             307511 non-null  object 
 13  NAME_EDUCATION_TYPE          307511 non-null  object 
 14  NAME_FAMILY_STATUS           307511 non-null  object 
 15  NAME_HOUSING_TYPE            307511 non-null  object 
 16  REGION_POPULATION_RELATIVE   307511 non-null  float64
 17  DAYS_BIRTH                   307511 non-null  int64  
 18  DAYS_EMPLOYED                307511 non-null  int64  
 19  DAYS_REGISTRATION            307511 non-null  float64
 20  DAYS_ID_PUBLISH              307511 non-null  int64  
 21  FLAG_MOBIL                   307511 non-null  int64  
 22  FLAG_EMP_PHONE               307511 non-null  int64  
 23  FLAG_WORK_PHONE              307511 non-null  int64  
 24  FLAG_CONT_MOBILE             307511 non-null  int64  
 25  FLAG_PHONE                   307511 non-null  int64  
 26  FLAG_EMAIL                   307511 non-null  int64  
 27  OCCUPATION_TYPE              307511 non-null  object 
 28  CNT_FAM_MEMBERS              307511 non-null  float64
 29  REGION_RATING_CLIENT         307511 non-null  int64  
 30  REGION_RATING_CLIENT_W_CITY  307511 non-null  int64  
 31  WEEKDAY_APPR_PROCESS_START   307511 non-null  object 
 32  HOUR_APPR_PROCESS_START      307511 non-null  int64  
 33  REG_REGION_NOT_LIVE_REGION   307511 non-null  int64  
 34  REG_REGION_NOT_WORK_REGION   307511 non-null  int64  
 35  LIVE_REGION_NOT_WORK_REGION  307511 non-null  int64  
 36  REG_CITY_NOT_LIVE_CITY       307511 non-null  int64  
 37  REG_CITY_NOT_WORK_CITY       307511 non-null  int64  
 38  LIVE_CITY_NOT_WORK_CITY      307511 non-null  int64  
 39  ORGANIZATION_TYPE            307511 non-null  object 
 40  EXT_SOURCE_2                 307511 non-null  float64
 41  EXT_SOURCE_3                 307511 non-null  float64
 42  DAYS_LAST_PHONE_CHANGE       307511 non-null  float64
dtypes: float64(10), int64(21), object(12)
memory usage: 100.9+ MB


dayandcount=['CNT_FAM_MEMBERS','DAYS_REGISTRATION','DAYS_LAST_PHONE_CHANGE']
application_data_up.loc[:, dayandcount]=application_data_up.loc[:,dayandcount].apply(lambda x: x.astype('int64',errors='ignore'))


application_data_up.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Data columns (total 43 columns):
 #   Column                       Non-Null Count   Dtype  
---  ------                       --------------   -----  
 0   SK_ID_CURR                   307511 non-null  int64  
 1   TARGET                       307511 non-null  int64  
 2   NAME_CONTRACT_TYPE           307511 non-null  object 
 3   CODE_GENDER                  307511 non-null  object 
 4   FLAG_OWN_CAR                 307511 non-null  object 
 5   FLAG_OWN_REALTY              307511 non-null  object 
 6   CNT_CHILDREN                 307511 non-null  int64  
 7   AMT_INCOME_TOTAL             307511 non-null  float64
 8   AMT_CREDIT                   307511 non-null  float64
 9   AMT_ANNUITY                  307511 non-null  float64
 10  AMT_GOODS_PRICE              307511 non-null  float64
 11  NAME_TYPE_SUITE              307511 non-null  object 
 12  NAME_INCOME_TYPE             307511 non-null  object 
 13  NAME_EDUCATION_TYPE          307511 non-null  object 
 14  NAME_FAMILY_STATUS           307511 non-null  object 
 15  NAME_HOUSING_TYPE            307511 non-null  object 
 16  REGION_POPULATION_RELATIVE   307511 non-null  float64
 17  DAYS_BIRTH                   307511 non-null  int64  
 18  DAYS_EMPLOYED                307511 non-null  int64  
 19  DAYS_REGISTRATION            307511 non-null  int64  
 20  DAYS_ID_PUBLISH              307511 non-null  int64  
 21  FLAG_MOBIL                   307511 non-null  int64  
 22  FLAG_EMP_PHONE               307511 non-null  int64  
 23  FLAG_WORK_PHONE              307511 non-null  int64  
 24  FLAG_CONT_MOBILE             307511 non-null  int64  
 25  FLAG_PHONE                   307511 non-null  int64  
 26  FLAG_EMAIL                   307511 non-null  int64  
 27  OCCUPATION_TYPE              307511 non-null  object 
 28  CNT_FAM_MEMBERS              307511 non-null  int64  
 29  REGION_RATING_CLIENT         307511 non-null  int64  
 30  REGION_RATING_CLIENT_W_CITY  307511 non-null  int64  
 31  WEEKDAY_APPR_PROCESS_START   307511 non-null  object 
 32  HOUR_APPR_PROCESS_START      307511 non-null  int64  
 33  REG_REGION_NOT_LIVE_REGION   307511 non-null  int64  
 34  REG_REGION_NOT_WORK_REGION   307511 non-null  int64  
 35  LIVE_REGION_NOT_WORK_REGION  307511 non-null  int64  
 36  REG_CITY_NOT_LIVE_CITY       307511 non-null  int64  
 37  REG_CITY_NOT_WORK_CITY       307511 non-null  int64  
 38  LIVE_CITY_NOT_WORK_CITY      307511 non-null  int64  
 39  ORGANIZATION_TYPE            307511 non-null  object 
 40  EXT_SOURCE_2                 307511 non-null  float64
 41  EXT_SOURCE_3                 307511 non-null  float64
 42  DAYS_LAST_PHONE_CHANGE       307511 non-null  int64  
dtypes: float64(7), int64(24), object(12)
memory usage: 100.9+ MB


#LISTING OBJECT TYPE COLUMNS AND CONFIRMING THE VALUES TO BE IN STRING TYPE

obj_col=list(application_data_up.select_dtypes(include='object').columns)

application_data_up.loc[:,obj_col]=application_data_up.loc[:,obj_col].apply(lambda x: x.astype('str'))


application_data_up.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Data columns (total 43 columns):
 #   Column                       Non-Null Count   Dtype  
---  ------                       --------------   -----  
 0   SK_ID_CURR                   307511 non-null  int64  
 1   TARGET                       307511 non-null  int64  
 2   NAME_CONTRACT_TYPE           307511 non-null  object 
 3   CODE_GENDER                  307511 non-null  object 
 4   FLAG_OWN_CAR                 307511 non-null  object 
 5   FLAG_OWN_REALTY              307511 non-null  object 
 6   CNT_CHILDREN                 307511 non-null  int64  
 7   AMT_INCOME_TOTAL             307511 non-null  float64
 8   AMT_CREDIT                   307511 non-null  float64
 9   AMT_ANNUITY                  307511 non-null  float64
 10  AMT_GOODS_PRICE              307511 non-null  float64
 11  NAME_TYPE_SUITE              307511 non-null  object 
 12  NAME_INCOME_TYPE             307511 non-null  object 
 13  NAME_EDUCATION_TYPE          307511 non-null  object 
 14  NAME_FAMILY_STATUS           307511 non-null  object 
 15  NAME_HOUSING_TYPE            307511 non-null  object 
 16  REGION_POPULATION_RELATIVE   307511 non-null  float64
 17  DAYS_BIRTH                   307511 non-null  int64  
 18  DAYS_EMPLOYED                307511 non-null  int64  
 19  DAYS_REGISTRATION            307511 non-null  int64  
 20  DAYS_ID_PUBLISH              307511 non-null  int64  
 21  FLAG_MOBIL                   307511 non-null  int64  
 22  FLAG_EMP_PHONE               307511 non-null  int64  
 23  FLAG_WORK_PHONE              307511 non-null  int64  
 24  FLAG_CONT_MOBILE             307511 non-null  int64  
 25  FLAG_PHONE                   307511 non-null  int64  
 26  FLAG_EMAIL                   307511 non-null  int64  
 27  OCCUPATION_TYPE              307511 non-null  object 
 28  CNT_FAM_MEMBERS              307511 non-null  int64  
 29  REGION_RATING_CLIENT         307511 non-null  int64  
 30  REGION_RATING_CLIENT_W_CITY  307511 non-null  int64  
 31  WEEKDAY_APPR_PROCESS_START   307511 non-null  object 
 32  HOUR_APPR_PROCESS_START      307511 non-null  int64  
 33  REG_REGION_NOT_LIVE_REGION   307511 non-null  int64  
 34  REG_REGION_NOT_WORK_REGION   307511 non-null  int64  
 35  LIVE_REGION_NOT_WORK_REGION  307511 non-null  int64  
 36  REG_CITY_NOT_LIVE_CITY       307511 non-null  int64  
 37  REG_CITY_NOT_WORK_CITY       307511 non-null  int64  
 38  LIVE_CITY_NOT_WORK_CITY      307511 non-null  int64  
 39  ORGANIZATION_TYPE            307511 non-null  object 
 40  EXT_SOURCE_2                 307511 non-null  float64
 41  EXT_SOURCE_3                 307511 non-null  float64
 42  DAYS_LAST_PHONE_CHANGE       307511 non-null  int64  
dtypes: float64(7), int64(24), object(12)
memory usage: 100.9+ MB


# VALUE COUNTS OF GENDER CODE

application_data_up.CODE_GENDER.value_counts()

F      202448
M      105059
XNA         4
Name: CODE_GENDER, dtype: int64


#CHECKING GENDER COLUMN

plt.figure(figsize=[20,10])
sns.barplot(x=application_data_up.CODE_GENDER, y=application_data_up.TARGET).set_title("GENDER VS TARGET",fontsize=30, 
                                                                                       color='Blue', pad=25)
plt.show()


round(application_data_up.ORGANIZATION_TYPE.value_counts()/application_data_up.shape[0]*100,2)

Business Entity Type 3    22.11
XNA                       18.01
Self-employed             12.49
Other                      5.43
Medicine                   3.64
Business Entity Type 2     3.43
Government                 3.38
School                     2.89
Trade: type 7              2.55
Kindergarten               2.24
Construction               2.19
Business Entity Type 1     1.95
Transport: type 4          1.76
Trade: type 3              1.14
Industry: type 9           1.10
Industry: type 3           1.07
Security                   1.06
Housing                    0.96
Industry: type 11          0.88
Military                   0.86
Bank                       0.82
Agriculture                0.80
Police                     0.76
Transport: type 2          0.72
Postal                     0.70
Security Ministries        0.64
Trade: type 2              0.62
Restaurant                 0.59
Services                   0.51
University                 0.43
Industry: type 7           0.43
Transport: type 3          0.39
Industry: type 1           0.34
Hotel                      0.31
Electricity                0.31
Industry: type 4           0.29
Trade: type 6              0.21
Industry: type 5           0.19
Insurance                  0.19
Telecom                    0.19
Emergency                  0.18
Industry: type 2           0.15
Advertising                0.14
Realtor                    0.13
Culture                    0.12
Industry: type 12          0.12
Trade: type 1              0.11
Mobile                     0.10
Legal Services             0.10
Cleaning                   0.08
Transport: type 1          0.07
Industry: type 6           0.04
Industry: type 10          0.04
Religion                   0.03
Industry: type 13          0.02
Trade: type 4              0.02
Trade: type 5              0.02
Industry: type 8           0.01
Name: ORGANIZATION_TYPE, dtype: float64


#Therefore, we eliminate the sub-category with the overall category

application_data_up.ORGANIZATION_TYPE = application_data_up.ORGANIZATION_TYPE.apply(lambda x: 'Industry' if 'Industry' in x else x)
application_data_up.ORGANIZATION_TYPE = application_data_up.ORGANIZATION_TYPE.apply(lambda x: 'Trade' if 'Trade' in x else x)
application_data_up.ORGANIZATION_TYPE = application_data_up.ORGANIZATION_TYPE.apply(lambda x: 'Transport' if 'Transport' in x else x)
application_data_up.ORGANIZATION_TYPE = application_data_up.ORGANIZATION_TYPE.apply(lambda x: 'Business' if 'Business' in x else x)


round(application_data_up.ORGANIZATION_TYPE.value_counts()/application_data_up.shape[0]*100,2)

Business               27.49
XNA                    18.01
Self-employed          12.49
Other                   5.43
Trade                   4.66
Industry                4.65
Medicine                3.64
Government              3.38
Transport               2.92
School                  2.89
Kindergarten            2.24
Construction            2.19
Security                1.06
Housing                 0.96
Military                0.86
Bank                    0.82
Agriculture             0.80
Police                  0.76
Postal                  0.70
Security Ministries     0.64
Restaurant              0.59
Services                0.51
University              0.43
Hotel                   0.31
Electricity             0.31
Insurance               0.19
Telecom                 0.19
Emergency               0.18
Advertising             0.14
Realtor                 0.13
Culture                 0.12
Mobile                  0.10
Legal Services          0.10
Cleaning                0.08
Religion                0.03
Name: ORGANIZATION_TYPE, dtype: float64


plt.figure(figsize=[30,10])

sns.barplot(x=application_data_up.ORGANIZATION_TYPE.value_counts().index,
            y=application_data_up.ORGANIZATION_TYPE.value_counts().values).set_title("Distribution within ORGANIZATION_TYPE",
                                                                            fontsize=20,color='Blue',pad=20)
    
plt.xlabel('ORGANIZATION_TYPE',fontsize=18,color='Black')
plt.xticks(rotation = 90)

plt.show()


application_data_up.NAME_CONTRACT_TYPE.value_counts()

Cash loans         278232
Revolving loans     29279
Name: NAME_CONTRACT_TYPE, dtype: int64


application_data_up.FLAG_OWN_CAR.value_counts()

N    202924
Y    104587
Name: FLAG_OWN_CAR, dtype: int64


application_data_up.FLAG_OWN_REALTY.value_counts()

Y    213312
N     94199
Name: FLAG_OWN_REALTY, dtype: int64


application_data_up.NAME_TYPE_SUITE.value_counts()

Unaccompanied      249818
Family              40149
Spouse, partner     11370
Children             3267
Other_B              1770
Other_A               866
Group of people       271
Name: NAME_TYPE_SUITE, dtype: int64


application_data_up.NAME_EDUCATION_TYPE.value_counts()

Secondary / secondary special    218391
Higher education                  74863
Incomplete higher                 10277
Lower secondary                    3816
Academic degree                     164
Name: NAME_EDUCATION_TYPE, dtype: int64


application_data_up.NAME_INCOME_TYPE.value_counts()

Working                 158774
Commercial associate     71617
Pensioner                55362
State servant            21703
Unemployed                  22
Student                     18
Businessman                 10
Maternity leave              5
Name: NAME_INCOME_TYPE, dtype: int64


application_data_up.NAME_FAMILY_STATUS.value_counts()

Married                 196432
Single / not married     45444
Civil marriage           29775
Separated                19770
Widow                    16088
Unknown                      2
Name: NAME_FAMILY_STATUS, dtype: int64


application_data_up.NAME_HOUSING_TYPE.value_counts()

House / apartment      272868
With parents            14840
Municipal apartment     11183
Rented apartment         4881
Office apartment         2617
Co-op apartment          1122
Name: NAME_HOUSING_TYPE, dtype: int64


application_data_up.WEEKDAY_APPR_PROCESS_START.value_counts()

TUESDAY      53901
WEDNESDAY    51934
MONDAY       50714
THURSDAY     50591
FRIDAY       50338
SATURDAY     33852
SUNDAY       16181
Name: WEEKDAY_APPR_PROCESS_START, dtype: int64


application_data_up.DAYS_BIRTH.unique()

array([ -9461, -16765, -19046, ...,  -7951,  -7857, -25061], dtype=int64)


application_data_up.DAYS_EMPLOYED.unique()

array([  -637,  -1188,   -225, ..., -12971, -11084,  -8694], dtype=int64)


#CHECKING 'DAYS_REGISTRATION' COLUMN

application_data_up.DAYS_REGISTRATION.unique()

array([ -3648,  -1186,  -4260, ..., -16396, -14558, -14798], dtype=int64)


#CHECKING 'DAYS_ID_PUBLISH' COLUMN

application_data_up.DAYS_ID_PUBLISH.unique()

array([-2120,  -291, -2531, ..., -6194, -5854, -6211], dtype=int64)


#CHECKING 'DAYS_LAST_PHONE_CHANGE' COLUMN

application_data_up.DAYS_LAST_PHONE_CHANGE.unique()

array([-1134,  -828,  -815, ..., -3988, -3899, -3538], dtype=int64)


(application_data_up["DAYS_EMPLOYED"]//365).value_counts().tail()

-49    4
-47    4
 0     2
-48    1
-50    1
Name: DAYS_EMPLOYED, dtype: int64


#changing values to positive integer

num_days=['DAYS_BIRTH','DAYS_EMPLOYED','DAYS_REGISTRATION','DAYS_ID_PUBLISH','DAYS_LAST_PHONE_CHANGE']

for i in num_days:
    application_data_up[i]=abs(application_data_up[i])


application_data_up.DAYS_LAST_PHONE_CHANGE.unique()

array([1134,  828,  815, ..., 3988, 3899, 3538], dtype=int64)


application_data_up.AMT_INCOME_TOTAL.quantile([0,0.1,0.15,0.2,0.25,0.3,0.4,0.5,0.6,0.7,0.75,0.8,0.9,0.95,0.99,0.999,1])

0.000        25650.0
0.100        81000.0
0.150        90000.0
0.200        99000.0
0.250       112500.0
0.300       112500.0
0.400       135000.0
0.500       147150.0
0.600       162000.0
0.700       180000.0
0.750       202500.0
0.800       225000.0
0.900       270000.0
0.950       337500.0
0.990       472500.0
0.999       900000.0
1.000    117000000.0
Name: AMT_INCOME_TOTAL, dtype: float64


application_data_up['INCOME_SLAB']=pd.qcut(application_data_up['AMT_INCOME_TOTAL'], q=[0,0.2,0.5,0.75,0.95,1],
                                          labels=['VeryLow','Low','Medium','High','VeryHigh'])


application_data_up['INCOME_SLAB'].value_counts()

Low         90089
Medium      82213
VeryLow     63671
High        57503
VeryHigh    14035
Name: INCOME_SLAB, dtype: int64


application_data_up['AMT_CREDIT_slab']=pd.qcut(application_data_up['AMT_CREDIT'], q=[0,0.2,0.5,0.75,0.95,1],
                                          labels=['VeryLow','Low','Medium','High','VeryHigh'])


application_data_up['AMT_CREDIT_slab'].value_counts()

Low         88924
Medium      77786
VeryLow     64925
High        61842
VeryHigh    14034
Name: AMT_CREDIT_slab, dtype: int64


#BINNING OF DAYS_BIRTH / AGE COLUMN
#CONVERTING DAYS TO YEARS

application_data_up['AGE']=(application_data_up['DAYS_BIRTH']//365).astype('int64',errors='ignore')


#MAX & MIN VALUES

print(application_data_up['AGE'].max())
print(application_data_up['AGE'].min())

69
20


#CREATING 10 BINS

application_data_up['AGE_BINS']=pd.cut(application_data_up['AGE'],bins=np.arange(20,71,5))


application_data_up['AGE_BINS'].value_counts()

(35, 40]    43680
(40, 45]    39997
(30, 35]    39437
(25, 30]    36488
(50, 55]    35097
(45, 50]    34404
(55, 60]    32722
(60, 65]    24359
(20, 25]    16317
(65, 70]     5009
Name: AGE_BINS, dtype: int64


#CHECKING AMT_INCOME_TOTAL_COLUMN
application_data_up.AMT_INCOME_TOTAL.quantile([0.9991,0.9992,0.9993])

0.9991    9.025105e+05
0.9992    1.034820e+06
0.9993    1.125000e+06
Name: AMT_INCOME_TOTAL, dtype: float64


#Handling outliers for the AMT_INCOME_TOTAL by capping the income to 10Lakh: - Coping with threshold value

application_data_up['AMT_INCOME_TOTAL']= np.where(application_data_up['AMT_INCOME_TOTAL']
                                                  >1000000,1000000,application_data_up['AMT_INCOME_TOTAL'])


application_data_up['AMT_INCOME_TOTAL'].value_counts()

135000.0    35750
112500.0    31019
157500.0    26556
180000.0    24719
90000.0     22483
            ...  
117324.0        1
64584.0         1
142897.5        1
109170.0        1
113062.5        1
Name: AMT_INCOME_TOTAL, Length: 2499, dtype: int64


application_data_up['AMT_INCOME_TOTAL'].max()

1000000.0


plt.figure(figsize=[20,5])

plt.subplot(1,2,1)
sns.boxplot(application_data_up['AMT_INCOME_TOTAL']).set_title("AMT_INCOME_TOTAL - BOX PLOT",fontsize=30,color='blue',pad=30)

plt.subplot(1,2,2)
sns.distplot(application_data_up['AMT_INCOME_TOTAL'],color='blue').set_title("AMT_INCOME_TOTAL - DISTRIBUTION",
                                                                     fontsize=30,color='blue',pad=30)

plt.show()


application_data_up.shape

(307511, 47)


application_data_up.shape

(307511, 47)


Q3=np.quantile(application_data_up['AMT_INCOME_TOTAL'],0.75)
Q1=np.quantile(application_data_up['AMT_INCOME_TOTAL'],0.25)
IQR= Q3-Q1
lower_range=Q1-1.5*IQR
upper_range=Q3+1.5*IQR

#outliers=round(application_data_up.loc[(application_data_up['AMT_INCOME_TOTAL']> upper_range)].count()/new_credit.shape[0]*100)
#outliers
application_data_up.loc[(application_data_up['AMT_INCOME_TOTAL']>upper_range)].shape

(14035, 47)


def remov_outliers(data,col):
    lower_range=0
    upper_range=0
    Q3=np.quantile(data[col],0.75)
    Q1=np.quantile(data[col],0.25)
    IQR=Q3-Q1
    lower_range=Q1-1.5*IQR
    upper_range=Q3-1.5*IQR
    return upper_range,lower_range


#impute outliers with median
application_data_up['AMT_INCOME_TOTAL']=np.where(application_data_up['AMT_INCOME_TOTAL']
                >upper_range,application_data_up['AMT_INCOME_TOTAL'].median(),application_data_up['AMT_INCOME_TOTAL'])


application_data_up.shape

(307511, 47)


sns.boxplot(application_data_up['AMT_INCOME_TOTAL']).set_title("AMT_INCOME_TOTAL - BOX PLOT", fontsize=25, color='blue', pad=20)
plt.show()


application_data_up.shape

(307511, 47)


plt.figure(figsize=[30,10])

plt.subplot(1,2,1)
sns.boxplot(application_data_up['AMT_INCOME_TOTAL']).set_title("AMT_INCOME_TOTAL - BOX PLOT", fontsize=25, color='blue',pad=30)

plt.subplot(1,2,2)
sns.distplot(application_data_up['AMT_INCOME_TOTAL'],color='blue').set_title("AMT_INCOME_TOTAL - DISTRIBUTION",
                                                                            fontsize=25, color='blue', pad=25)
plt.show()


plt.figure(figsize=[20,5])

plt.subplot(1,2,1)
sns.boxplot(application_data_up['AMT_ANNUITY']).set_title("AMT_ANNUITY - BOX PLOT",fontsize=25,color='blue',pad=20)

plt.subplot(1,2,2)
sns.distplot(application_data_up['AMT_ANNUITY'],color='blue').set_title("AMT_ANNUITY - DISTRIBUTION",
                                                                     fontsize=25,color='indigo',pad=20)

plt.show()


application_data_up.shape

(307511, 47)


plt.figure(figsize=[30,10])

plt.subplot(1,2,1)
sns.boxplot(application_data_up['AMT_ANNUITY']).set_title("AMT_ANNUITY - BOX PLOT",fontsize=15,color='blue',pad=20)

plt.subplot(1,2,2)
sns.distplot(application_data_up['AMT_ANNUITY'],color='blue').set_title("AMT_ANNUITY - DISTRIBUTION",
                                                                     fontsize=15,color='blue',pad=20)
plt.show()


plt.figure(figsize=[30,10])

plt.subplot(1,2,1)
sns.boxplot(application_data_up['AGE']).set_title("AGE - BOXPLOT",fontsize=25,color='blue',pad=30)

plt.subplot(1,2,2)
sns.distplot(application_data_up['AGE'],color='blue').set_title("AGE - DISTRIBUTION",
                                                                     fontsize=25,color='blue',pad=30)
plt.show()


plt.figure(figsize=[20,5])

plt.subplot(1,2,1)
sns.boxplot(application_data_up['DAYS_EMPLOYED']).set_title("DAYS_EMPLOYED - BOX PLOT",fontsize=25,color='blue',pad=30)

plt.subplot(1,2,2)
sns.distplot(application_data_up['DAYS_EMPLOYED'],color='blue').set_title("DAYS_EMPLOYED - DISTRIBUTION",
                                                                     fontsize=25,color='blue',pad=30)

plt.show()


application_data_up['DAYS_EMPLOYED']=np.where(application_data_up['DAYS_EMPLOYED']
                        >=100000,application_data_up['DAYS_EMPLOYED'].median(),application_data_up['DAYS_EMPLOYED'])


application_data_up['DAYS_EMPLOYED']=np.where(application_data_up['DAYS_EMPLOYED']//365>=80,application_data_up['DAYS_EMPLOYED'].median(),application_data_up['DAYS_EMPLOYED'])


application_data_up.shape

(307511, 47)


plt.figure(figsize=[20,5])

plt.subplot(1,2,1)
sns.boxplot(application_data_up['DAYS_EMPLOYED']).set_title("DAYS_EMPLOYED - BOX PLOT",fontsize=25,color='blue',pad=30)

plt.subplot(1,2,2)
sns.distplot(application_data_up['DAYS_EMPLOYED'],color='blue').set_title("DAYS_EMPLOYED - DISTRIBUTION",
                                                                     fontsize=25,color='blue',pad=30)

plt.show()


#CHECKING DAYS_REGISTRATION COLUMN
plt.figure(figsize=[20,5])

plt.subplot(1,2,1)
sns.boxplot(application_data_up['DAYS_REGISTRATION']).set_title("DAYS_REGISTRATION - BOX PLOT",fontsize=25,color='blue',pad=30)

plt.subplot(1,2,2)
sns.distplot(application_data_up['DAYS_REGISTRATION'],color='blue').set_title("DAYS_REGISTRATION - DISTRIBUTION",
                                                                     fontsize=25,color='blue',pad=30)

plt.show()


#CREATING ADDITIONAL COLUMNS FOR ANALYSIS
#CREATING A COLUMN WITH VALUES CREDIT TO INCOME RATIO - Derived Metrics

application_data_up['CREDIT_RATIO']=(application_data_up.AMT_CREDIT // application_data_up.AMT_INCOME_TOTAL).astype('int64')


application_data_up.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Data columns (total 48 columns):
 #   Column                       Non-Null Count   Dtype   
---  ------                       --------------   -----   
 0   SK_ID_CURR                   307511 non-null  int64   
 1   TARGET                       307511 non-null  int64   
 2   NAME_CONTRACT_TYPE           307511 non-null  object  
 3   CODE_GENDER                  307511 non-null  object  
 4   FLAG_OWN_CAR                 307511 non-null  object  
 5   FLAG_OWN_REALTY              307511 non-null  object  
 6   CNT_CHILDREN                 307511 non-null  int64   
 7   AMT_INCOME_TOTAL             307511 non-null  float64 
 8   AMT_CREDIT                   307511 non-null  float64 
 9   AMT_ANNUITY                  307511 non-null  float64 
 10  AMT_GOODS_PRICE              307511 non-null  float64 
 11  NAME_TYPE_SUITE              307511 non-null  object  
 12  NAME_INCOME_TYPE             307511 non-null  object  
 13  NAME_EDUCATION_TYPE          307511 non-null  object  
 14  NAME_FAMILY_STATUS           307511 non-null  object  
 15  NAME_HOUSING_TYPE            307511 non-null  object  
 16  REGION_POPULATION_RELATIVE   307511 non-null  float64 
 17  DAYS_BIRTH                   307511 non-null  int64   
 18  DAYS_EMPLOYED                307511 non-null  float64 
 19  DAYS_REGISTRATION            307511 non-null  int64   
 20  DAYS_ID_PUBLISH              307511 non-null  int64   
 21  FLAG_MOBIL                   307511 non-null  int64   
 22  FLAG_EMP_PHONE               307511 non-null  int64   
 23  FLAG_WORK_PHONE              307511 non-null  int64   
 24  FLAG_CONT_MOBILE             307511 non-null  int64   
 25  FLAG_PHONE                   307511 non-null  int64   
 26  FLAG_EMAIL                   307511 non-null  int64   
 27  OCCUPATION_TYPE              307511 non-null  object  
 28  CNT_FAM_MEMBERS              307511 non-null  int64   
 29  REGION_RATING_CLIENT         307511 non-null  int64   
 30  REGION_RATING_CLIENT_W_CITY  307511 non-null  int64   
 31  WEEKDAY_APPR_PROCESS_START   307511 non-null  object  
 32  HOUR_APPR_PROCESS_START      307511 non-null  int64   
 33  REG_REGION_NOT_LIVE_REGION   307511 non-null  int64   
 34  REG_REGION_NOT_WORK_REGION   307511 non-null  int64   
 35  LIVE_REGION_NOT_WORK_REGION  307511 non-null  int64   
 36  REG_CITY_NOT_LIVE_CITY       307511 non-null  int64   
 37  REG_CITY_NOT_WORK_CITY       307511 non-null  int64   
 38  LIVE_CITY_NOT_WORK_CITY      307511 non-null  int64   
 39  ORGANIZATION_TYPE            307511 non-null  object  
 40  EXT_SOURCE_2                 307511 non-null  float64 
 41  EXT_SOURCE_3                 307511 non-null  float64 
 42  DAYS_LAST_PHONE_CHANGE       307511 non-null  int64   
 43  INCOME_SLAB                  307511 non-null  category
 44  AMT_CREDIT_slab              307511 non-null  category
 45  AGE                          307511 non-null  int64   
 46  AGE_BINS                     307510 non-null  category
 47  CREDIT_RATIO                 307511 non-null  int64   
dtypes: category(3), float64(8), int64(25), object(12)
memory usage: 106.5+ MB


##CHECKING TARGET IMBALANCE

go.Figure(data=[go.Pie(labels=application_data_up.TARGET.value_counts(normalize=True).index,
                       values=application_data_up.TARGET.value_counts(normalize=True).values, hole=.5,
                      title='Defaulter Vs Non-Defaulters')])


##CHECKING GENDER IMBALANCE

go.Figure(data=[go.Pie(labels=application_data_up.CODE_GENDER.value_counts(normalize=True).index,
                       values=application_data_up.CODE_GENDER.value_counts(normalize=True).values, hole=.5,
                      title='Male Vs Female')])


plt.figure(figsize=[12,12])

f=sns.heatmap(application_data_up[['AMT_CREDIT','AMT_GOODS_PRICE','AMT_ANNUITY','AMT_INCOME_TOTAL','AGE','DAYS_BIRTH',
                          'DAYS_EMPLOYED','REGION_RATING_CLIENT','REGION_RATING_CLIENT_W_CITY']].corr(),
              cmap="Blues",annot=True).set_title('Correlation between variables',fontsize=20,color='blue')

plt.show()


plt.figure(figsize=[12,12])

f=sns.heatmap(application_data_up[['AGE','FLAG_EMP_PHONE','DAYS_REGISTRATION','DAYS_ID_PUBLISH','REGION_RATING_CLIENT',
                          'REGION_RATING_CLIENT_W_CITY','REG_CITY_NOT_LIVE_CITY','REG_CITY_NOT_WORK_CITY',
                           'LIVE_CITY_NOT_WORK_CITY']].corr(),
              cmap="Oranges",annot=True).set_title('Correlation between variables',fontsize=15,color='NAvy')

plt.show()


application_data_up['REG_CITY_NOT_LIVE_CITY'].value_counts()

0    283472
1     24039
Name: REG_CITY_NOT_LIVE_CITY, dtype: int64


application_data_up['AGE_BINS'].value_counts()

(35, 40]    43680
(40, 45]    39997
(30, 35]    39437
(25, 30]    36488
(50, 55]    35097
(45, 50]    34404
(55, 60]    32722
(60, 65]    24359
(20, 25]    16317
(65, 70]     5009
Name: AGE_BINS, dtype: int64


#Creating two datasets for target=1 and target=0 (1=bad,0=good)

target1=application_data_up[application_data_up['TARGET']==1]
target0=application_data_up[application_data_up['TARGET']==0]

print(target1.shape,target0.shape,application_data_up.shape)

(24825, 48) (282686, 48) (307511, 48)


print("The dataset with Target value 1 has :" + "{:.2%}".format(target1.shape[0]/application_data_up.shape[0])+ "data.")
print("The dataset with Target value 0 has :" + "{:.2%}".format(target0.shape[0]/application_data_up.shape[0])+ "data.")

The dataset with Target value 1 has :8.07%data.
The dataset with Target value 0 has :91.93%data.


#CHECKING THE GENDER COLUMNS

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
sns.barplot(x=target1.CODE_GENDER.value_counts(normalize=True).index,
          y=target1.CODE_GENDER.value_counts(normalize=True).values, 
           palette="rocket").set_title("Gender(Defulter)%" , fontsize=30, color="blue")

plt.subplot(1,2,2)
sns.barplot(x=target0.CODE_GENDER.value_counts(normalize=True).index,
           y=target0.CODE_GENDER.value_counts(normalize=True).values,
           palette="rocket").set_title("Gender(Non-Defulter)%" , fontsize=30, color="blue")
plt.show()


#checking NAME_CONTACT_TYPE

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
target1.NAME_CONTRACT_TYPE.value_counts(normalize=True).plot.barh(color="blue").set_title("NAME_CONTRACT_TYPE(DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.subplot(1,2,2)
target0.NAME_CONTRACT_TYPE.value_counts(normalize=True).plot.barh(color="red").set_title("NAME_CONTRACT_TYPE(NON-DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.show()


#Owing car vs Defaulting

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
target1.FLAG_OWN_CAR.value_counts(normalize=True).plot.barh(color="blue").set_title("FLAG_OWN_CAR(DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.subplot(1,2,2)
target0.FLAG_OWN_CAR.value_counts(normalize=True).plot.barh(color="red").set_title("FLAG_OWN_CAR(NON-DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.show()


#Owing a House/Estate etc vs Defaulting

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
target1.FLAG_OWN_REALTY.value_counts(normalize=True).plot.barh(color="blue").set_title("FLAG_OWN_REALTY(DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.subplot(1,2,2)
target0.FLAG_OWN_REALTY.value_counts(normalize=True).plot.barh(color="red").set_title("FLAG_OWN_REALTY(NON-DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.show()


#NAME_TYPE_SUITE vs Defaulting

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
target1.NAME_TYPE_SUITE.value_counts(normalize=True).plot.barh(color="blue").set_title("NAME_TYPE_SUITE(DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.subplot(1,2,2)
target0.NAME_TYPE_SUITE.value_counts(normalize=True).plot.barh(color="red").set_title("NAME_TYPE_SUITE(NON-DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.show()


#NAME_INCOME_TYPE vs Defaulting

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
target1.NAME_INCOME_TYPE.value_counts(normalize=True).plot.barh(color="blue").set_title("NAME_INCOME_TYPE(DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.subplot(1,2,2)
target0.NAME_INCOME_TYPE.value_counts(normalize=True).plot.barh(color="red").set_title("NAME_INCOME_TYPE(NON-DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.show()


#Education vs Defaulting

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
target1.NAME_EDUCATION_TYPE.value_counts(normalize=True).plot.barh(color="blue").set_title("NAME_EDUCATION_TYPE(DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.subplot(1,2,2)
target0.NAME_EDUCATION_TYPE.value_counts(normalize=True).plot.barh(color="red").set_title("NAME_EDUCATION_TYPE(NON-DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.show()


#Housing vs Defaulting

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
target1.NAME_HOUSING_TYPE.value_counts(normalize=True).plot.barh(color="blue").set_title("NAME_HOUSING_TYPE(DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.subplot(1,2,2)
target0.NAME_HOUSING_TYPE.value_counts(normalize=True).plot.barh(color="red").set_title("NAME_HOUSING_TYPE(NON-DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.show()


#Marital status vs Defaulting

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
target1.NAME_FAMILY_STATUS.value_counts(normalize=True).plot.barh(color="blue").set_title("NAME_FAMILY_STATUS(DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.subplot(1,2,2)
target0.NAME_FAMILY_STATUS.value_counts(normalize=True).plot.barh(color="red").set_title("NAME_FAMILY_STATUS(NON-DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.show()


#Occupations vs Defaulting

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
target1.OCCUPATION_TYPE.value_counts(normalize=True).plot.barh(color="blue").set_title("OCCUPATION_TYPE(DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.subplot(1,2,2)
target0.OCCUPATION_TYPE.value_counts(normalize=True).plot.barh(color="red").set_title("OCCUPATION_TYPE(NON-DEFAULTER)%",
                                                                                       fontsize=20,color="black", pad=30)
plt.show()


#Day of the week vs Defaulting

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
target1.WEEKDAY_APPR_PROCESS_START.value_counts(normalize=True).plot.barh(color="blue").set_title("WEEKDAY_APPR_PROCESS_START(DEFAULTER)%",
                                                                                       fontsize=10,color="black", pad=30)
plt.subplot(1,2,2)
target0.WEEKDAY_APPR_PROCESS_START.value_counts(normalize=True).plot.barh(color="red").set_title("WEEKDAY_APPR_PROCESS_START(NON-DEFAULTER)%",
                                                                                       fontsize=10,color="black", pad=30)
plt.show()


#Organization type vs Defaulting

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
target1.ORGANIZATION_TYPE.value_counts(normalize=True).plot.barh(color="blue").set_title("ORGANIZATION_TYPE(DEFAULTER)%",
                                                                                       fontsize=10,color="black", pad=30)
plt.subplot(1,2,2)
target0.ORGANIZATION_TYPE.value_counts(normalize=True).plot.barh(color="red").set_title("ORGANIZATION_TYPE(NON-DEFAULTER)%",
                                                                                       fontsize=10,color="black", pad=30)
plt.show()


#Income vs Defaulting

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
target1.INCOME_SLAB.value_counts(normalize=True).plot.barh(color="blue").set_title("INCOME_SLAB(DEFAULTER)%",
                                                                                       fontsize=10,color="black", pad=30)
plt.subplot(1,2,2)
target0.INCOME_SLAB.value_counts(normalize=True).plot.barh(color="red").set_title("INCOME_SLAB(NON-DEFAULTER)%",
                                                                                       fontsize=10,color="black", pad=30)
plt.show()


#Loan amount vs Defaulting

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
target1.AMT_CREDIT_slab.value_counts(normalize=True).plot.barh(color="blue").set_title("AMT_CREDIT_slab(DEFAULTER)%",
                                                                                       fontsize=10,color="black", pad=30)
plt.subplot(1,2,2)
target0.AMT_CREDIT_slab.value_counts(normalize=True).plot.barh(color="red").set_title("AMT_CREDIT_slab(NON-DEFAULTER)%",
                                                                                       fontsize=10,color="black", pad=30)
plt.show()


#Age groups vs Defaulting

plt.figure(figsize=[15,5])

plt.subplot(1,2,1)
target1.AGE_BINS.value_counts(normalize=True).plot.barh(color="blue").set_title("AGE_BINS(DEFAULTER)%",
                                                                                       fontsize=10,color="black", pad=30)
plt.subplot(1,2,2)
target0.AGE_BINS.value_counts(normalize=True).plot.barh(color="red").set_title("AGE_BINS(NON-DEFAULTER)%",
                                                                                       fontsize=10,color="black", pad=30)
plt.show()


target0.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 282686 entries, 1 to 307510
Data columns (total 48 columns):
 #   Column                       Non-Null Count   Dtype   
---  ------                       --------------   -----   
 0   SK_ID_CURR                   282686 non-null  int64   
 1   TARGET                       282686 non-null  int64   
 2   NAME_CONTRACT_TYPE           282686 non-null  object  
 3   CODE_GENDER                  282686 non-null  object  
 4   FLAG_OWN_CAR                 282686 non-null  object  
 5   FLAG_OWN_REALTY              282686 non-null  object  
 6   CNT_CHILDREN                 282686 non-null  int64   
 7   AMT_INCOME_TOTAL             282686 non-null  float64 
 8   AMT_CREDIT                   282686 non-null  float64 
 9   AMT_ANNUITY                  282686 non-null  float64 
 10  AMT_GOODS_PRICE              282686 non-null  float64 
 11  NAME_TYPE_SUITE              282686 non-null  object  
 12  NAME_INCOME_TYPE             282686 non-null  object  
 13  NAME_EDUCATION_TYPE          282686 non-null  object  
 14  NAME_FAMILY_STATUS           282686 non-null  object  
 15  NAME_HOUSING_TYPE            282686 non-null  object  
 16  REGION_POPULATION_RELATIVE   282686 non-null  float64 
 17  DAYS_BIRTH                   282686 non-null  int64   
 18  DAYS_EMPLOYED                282686 non-null  float64 
 19  DAYS_REGISTRATION            282686 non-null  int64   
 20  DAYS_ID_PUBLISH              282686 non-null  int64   
 21  FLAG_MOBIL                   282686 non-null  int64   
 22  FLAG_EMP_PHONE               282686 non-null  int64   
 23  FLAG_WORK_PHONE              282686 non-null  int64   
 24  FLAG_CONT_MOBILE             282686 non-null  int64   
 25  FLAG_PHONE                   282686 non-null  int64   
 26  FLAG_EMAIL                   282686 non-null  int64   
 27  OCCUPATION_TYPE              282686 non-null  object  
 28  CNT_FAM_MEMBERS              282686 non-null  int64   
 29  REGION_RATING_CLIENT         282686 non-null  int64   
 30  REGION_RATING_CLIENT_W_CITY  282686 non-null  int64   
 31  WEEKDAY_APPR_PROCESS_START   282686 non-null  object  
 32  HOUR_APPR_PROCESS_START      282686 non-null  int64   
 33  REG_REGION_NOT_LIVE_REGION   282686 non-null  int64   
 34  REG_REGION_NOT_WORK_REGION   282686 non-null  int64   
 35  LIVE_REGION_NOT_WORK_REGION  282686 non-null  int64   
 36  REG_CITY_NOT_LIVE_CITY       282686 non-null  int64   
 37  REG_CITY_NOT_WORK_CITY       282686 non-null  int64   
 38  LIVE_CITY_NOT_WORK_CITY      282686 non-null  int64   
 39  ORGANIZATION_TYPE            282686 non-null  object  
 40  EXT_SOURCE_2                 282686 non-null  float64 
 41  EXT_SOURCE_3                 282686 non-null  float64 
 42  DAYS_LAST_PHONE_CHANGE       282686 non-null  int64   
 43  INCOME_SLAB                  282686 non-null  category
 44  AMT_CREDIT_slab              282686 non-null  category
 45  AGE                          282686 non-null  int64   
 46  AGE_BINS                     282685 non-null  category
 47  CREDIT_RATIO                 282686 non-null  int64   
dtypes: category(3), float64(8), int64(25), object(12)
memory usage: 100.0+ MB


target0.shape

(282686, 48)


previous_application.head()


previous_application.shape

(1670214, 37)


previous_application.isnull().sum()

SK_ID_PREV                           0
SK_ID_CURR                           0
NAME_CONTRACT_TYPE                   0
AMT_ANNUITY                     372235
AMT_APPLICATION                      0
AMT_CREDIT                           1
AMT_DOWN_PAYMENT                895844
AMT_GOODS_PRICE                 385515
WEEKDAY_APPR_PROCESS_START           0
HOUR_APPR_PROCESS_START              0
FLAG_LAST_APPL_PER_CONTRACT          0
NFLAG_LAST_APPL_IN_DAY               0
RATE_DOWN_PAYMENT               895844
RATE_INTEREST_PRIMARY          1664263
RATE_INTEREST_PRIVILEGED       1664263
NAME_CASH_LOAN_PURPOSE               0
NAME_CONTRACT_STATUS                 0
DAYS_DECISION                        0
NAME_PAYMENT_TYPE                    0
CODE_REJECT_REASON                   0
NAME_TYPE_SUITE                 820405
NAME_CLIENT_TYPE                     0
NAME_GOODS_CATEGORY                  0
NAME_PORTFOLIO                       0
NAME_PRODUCT_TYPE                    0
CHANNEL_TYPE                         0
SELLERPLACE_AREA                     0
NAME_SELLER_INDUSTRY                 0
CNT_PAYMENT                     372230
NAME_YIELD_GROUP                     0
PRODUCT_COMBINATION                346
DAYS_FIRST_DRAWING              673065
DAYS_FIRST_DUE                  673065
DAYS_LAST_DUE_1ST_VERSION       673065
DAYS_LAST_DUE                   673065
DAYS_TERMINATION                673065
NFLAG_INSURED_ON_APPROVAL       673065
dtype: int64


req_columns=['SK_ID_CURR','AMT_APPLICATION','NAME_CASH_LOAN_PURPOSE','NAME_CONTRACT_STATUS','DAYS_DECISION','NAME_PAYMENT_TYPE',
            'CODE_REJECT_REASON','NAME_CLIENT_TYPE','NAME_GOODS_CATEGORY','NAME_PORTFOLIO','NAME_PRODUCT_TYPE','CHANNEL_TYPE',
            'NAME_YIELD_GROUP']
previous_application=previous_application.loc[:,req_columns]


previous_application.shape

(1670214, 13)


#checking for any missing values

previous_application.isnull().sum()

SK_ID_CURR                0
AMT_APPLICATION           0
NAME_CASH_LOAN_PURPOSE    0
NAME_CONTRACT_STATUS      0
DAYS_DECISION             0
NAME_PAYMENT_TYPE         0
CODE_REJECT_REASON        0
NAME_CLIENT_TYPE          0
NAME_GOODS_CATEGORY       0
NAME_PORTFOLIO            0
NAME_PRODUCT_TYPE         0
CHANNEL_TYPE              0
NAME_YIELD_GROUP          0
dtype: int64


previous_application.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 13 columns):
 #   Column                  Non-Null Count    Dtype  
---  ------                  --------------    -----  
 0   SK_ID_CURR              1670214 non-null  int64  
 1   AMT_APPLICATION         1670214 non-null  float64
 2   NAME_CASH_LOAN_PURPOSE  1670214 non-null  object 
 3   NAME_CONTRACT_STATUS    1670214 non-null  object 
 4   DAYS_DECISION           1670214 non-null  int64  
 5   NAME_PAYMENT_TYPE       1670214 non-null  object 
 6   CODE_REJECT_REASON      1670214 non-null  object 
 7   NAME_CLIENT_TYPE        1670214 non-null  object 
 8   NAME_GOODS_CATEGORY     1670214 non-null  object 
 9   NAME_PORTFOLIO          1670214 non-null  object 
 10  NAME_PRODUCT_TYPE       1670214 non-null  object 
 11  CHANNEL_TYPE            1670214 non-null  object 
 12  NAME_YIELD_GROUP        1670214 non-null  object 
dtypes: float64(1), int64(2), object(10)
memory usage: 165.7+ MB


previous_application.DAYS_DECISION.unique()

array([  -73,  -164,  -301, ..., -1967, -2389,    -1], dtype=int64)


#changing values to positive integer

previous_application['DAYS_DECISION']=abs(previous_application['DAYS_DECISION'])


previous_application.NAME_PAYMENT_TYPE.value_counts()

Cash through the bank                        1033552
XNA                                           627384
Non-cash from your account                      8193
Cashless from the account of the employer       1085
Name: NAME_PAYMENT_TYPE, dtype: int64


previous_application.NAME_CLIENT_TYPE.value_counts()

Repeater     1231261
New           301363
Refreshed     135649
XNA             1941
Name: NAME_CLIENT_TYPE, dtype: int64


previous_application.NAME_PORTFOLIO.value_counts()

POS      691011
Cash     461563
XNA      372230
Cards    144985
Cars        425
Name: NAME_PORTFOLIO, dtype: int64


#CHANGING XNA TO UNKNOWN

XNA_col=['NAME_PAYMENT_TYPE','NAME_CLIENT_TYPE','NAME_PORTFOLIO']

for i in XNA_col:
    previous_application[i]=previous_application[i].str.replace('XMA','unknown')


#plotting graph for amt_application

plt.figure(figsize=[20,6])

plt.subplot(1,2,1)
sns.boxplot(previous_application['AMT_APPLICATION']).set_title("AMT_APPLICATION - BOXPLOT", fontsize=20, color='blue', pad=20)
plt.subplot(1,2,2)
sns.distplot(previous_application['AMT_APPLICATION'],color="blue").set_title("AMT_APPLICATION - DISTRIBUTION",
                                                                             fontsize=20, color='red', pad=20)

plt.show()


#plotting graph for DAYS_DECISION

plt.figure(figsize=[20,6])

plt.subplot(1,2,1)
sns.boxplot(previous_application['DAYS_DECISION']).set_title("DAYS_DECISION - BOX PLOT",fontsize=20,color='blue',pad=20)

plt.subplot(1,2,2)
sns.distplot(previous_application['DAYS_DECISION'],color='red').set_title("DAYS_DECISION - DISTRIBUTION",
                                                                     fontsize=20,color='blue',pad=20)

plt.show()


#reason for load
plt.figure(figsize=[20,10])

previous_application.NAME_CASH_LOAN_PURPOSE.value_counts(normalize=True).plot.barh(color='blue').set_title("NAME_CASH_LOAN_PURPOSE",
                                                                     fontsize=20,color='blue',pad=20)
plt.show()


#since most of the values are missing,this column is not useful for our analysis

previous_application.drop(columns=['NAME_CASH_LOAN_PURPOSE'],inplace=True)


previous_application.shape

(1670214, 12)


#Payment method for loan

plt.figure(figsize=[20,10])

previous_application.NAME_PAYMENT_TYPE.value_counts(normalize=True).plot.barh(color="blue").set_title("Payment method for loan",
                                                                                       fontsize=15,color="black", pad=30)
plt.show()


#kind of goods the client applied for in the previous loan

plt.figure(figsize=[20,10])
previous_application.NAME_GOODS_CATEGORY.value_counts(normalize=True).plot.barh(color="blue").set_title("kind of goods the client applied for in the previous loan",
                                                                                       fontsize=15,color="black", pad=30)
plt.show()


previous_application.drop(columns=['NAME_GOODS_CATEGORY'],inplace=True)  #this
previous_application.shape

(1670214, 11)


#was the prev app for cash,pos,car

plt.figure(figsize=[20,10])


previous_application.NAME_PORTFOLIO.value_counts(normalize=True).plot.barh(color="Brown").set_title("REASON FOR PREV APP",
                                                                                       fontsize=15,color="black", pad=20)
plt.show()


#Through which channel we acquired the client on the previous application

plt.figure(figsize=[20,10])
previous_application.CHANNEL_TYPE.value_counts(normalize=True).plot.barh(color="blue").set_title("Through which channel we acquired the client on the previous application",
                                                                                       fontsize=15,color="black", pad=30)
plt.show()


#since this column will not add value to our analysis, we are dropping it....

previous_application.drop(columns=['NAME_YIELD_GROUP'],inplace=True)
previous_application.shape

(1670214, 10)


merged_df=pd.merge(left=application_data_up,right=previous_application,how='inner',on='SK_ID_CURR',suffixes='_x')


merged_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1413701 entries, 0 to 1413700
Data columns (total 57 columns):
 #   Column                       Non-Null Count    Dtype   
---  ------                       --------------    -----   
 0   SK_ID_CURR                   1413701 non-null  int64   
 1   TARGET                       1413701 non-null  int64   
 2   NAME_CONTRACT_TYPE           1413701 non-null  object  
 3   CODE_GENDER                  1413701 non-null  object  
 4   FLAG_OWN_CAR                 1413701 non-null  object  
 5   FLAG_OWN_REALTY              1413701 non-null  object  
 6   CNT_CHILDREN                 1413701 non-null  int64   
 7   AMT_INCOME_TOTAL             1413701 non-null  float64 
 8   AMT_CREDIT                   1413701 non-null  float64 
 9   AMT_ANNUITY                  1413701 non-null  float64 
 10  AMT_GOODS_PRICE              1413701 non-null  float64 
 11  NAME_TYPE_SUITE              1413701 non-null  object  
 12  NAME_INCOME_TYPE             1413701 non-null  object  
 13  NAME_EDUCATION_TYPE          1413701 non-null  object  
 14  NAME_FAMILY_STATUS           1413701 non-null  object  
 15  NAME_HOUSING_TYPE            1413701 non-null  object  
 16  REGION_POPULATION_RELATIVE   1413701 non-null  float64 
 17  DAYS_BIRTH                   1413701 non-null  int64   
 18  DAYS_EMPLOYED                1413701 non-null  float64 
 19  DAYS_REGISTRATION            1413701 non-null  int64   
 20  DAYS_ID_PUBLISH              1413701 non-null  int64   
 21  FLAG_MOBIL                   1413701 non-null  int64   
 22  FLAG_EMP_PHONE               1413701 non-null  int64   
 23  FLAG_WORK_PHONE              1413701 non-null  int64   
 24  FLAG_CONT_MOBILE             1413701 non-null  int64   
 25  FLAG_PHONE                   1413701 non-null  int64   
 26  FLAG_EMAIL                   1413701 non-null  int64   
 27  OCCUPATION_TYPE              1413701 non-null  object  
 28  CNT_FAM_MEMBERS              1413701 non-null  int64   
 29  REGION_RATING_CLIENT         1413701 non-null  int64   
 30  REGION_RATING_CLIENT_W_CITY  1413701 non-null  int64   
 31  WEEKDAY_APPR_PROCESS_START   1413701 non-null  object  
 32  HOUR_APPR_PROCESS_START      1413701 non-null  int64   
 33  REG_REGION_NOT_LIVE_REGION   1413701 non-null  int64   
 34  REG_REGION_NOT_WORK_REGION   1413701 non-null  int64   
 35  LIVE_REGION_NOT_WORK_REGION  1413701 non-null  int64   
 36  REG_CITY_NOT_LIVE_CITY       1413701 non-null  int64   
 37  REG_CITY_NOT_WORK_CITY       1413701 non-null  int64   
 38  LIVE_CITY_NOT_WORK_CITY      1413701 non-null  int64   
 39  ORGANIZATION_TYPE            1413701 non-null  object  
 40  EXT_SOURCE_2                 1413701 non-null  float64 
 41  EXT_SOURCE_3                 1413701 non-null  float64 
 42  DAYS_LAST_PHONE_CHANGE       1413701 non-null  int64   
 43  INCOME_SLAB                  1413701 non-null  category
 44  AMT_CREDIT_slab              1413701 non-null  category
 45  AGE                          1413701 non-null  int64   
 46  AGE_BINS                     1413697 non-null  category
 47  CREDIT_RATIO                 1413701 non-null  int64   
 48  AMT_APPLICATION              1413701 non-null  float64 
 49  NAME_CONTRACT_STATUS         1413701 non-null  object  
 50  DAYS_DECISION                1413701 non-null  int64   
 51  NAME_PAYMENT_TYPE            1413701 non-null  object  
 52  CODE_REJECT_REASON           1413701 non-null  object  
 53  NAME_CLIENT_TYPE             1413701 non-null  object  
 54  NAME_PORTFOLIO               1413701 non-null  object  
 55  NAME_PRODUCT_TYPE            1413701 non-null  object  
 56  CHANNEL_TYPE                 1413701 non-null  object  
dtypes: category(3), float64(9), int64(26), object(19)
memory usage: 597.3+ MB


# %of Loan Payment Difficulties for NAME_CONTRACT_STATUS and NAME_CLIENT_TYPE

table=pd.pivot_table(merged_df, values='TARGET', index=['NAME_CLIENT_TYPE'],
                    columns=['NAME_CONTRACT_STATUS'], aggfunc=np.mean)
cm=sns.light_palette("blue", as_cmap=True)
table.style.background_gradient(cmap=cm)


table.T.plot(kind='bar').set_ylabel('% of Loan-Payment Difficulties')

plt.title('% of Loan Payment Difficulties for NAME_CONTRACT_STATUS and NAME_CLIENT_TYPE',fontdict={'fontsize':18},pad=20)

plt.show()


# %of Loan Payment Difficulties for NAME_CONTRACT_STATUS and NAME_CLIENT_TYPE

table=pd.pivot_table(merged_df, values='TARGET', index=['NAME_CONTRACT_TYPE'],
                    columns=['NAME_CONTRACT_STATUS'], aggfunc=np.mean)
cm=sns.light_palette("blue", as_cmap=True)
table.style.background_gradient(cmap=cm)


table.T.plot(kind='bar').set_ylabel('% of Loan-Payment Difficulties')

plt.title('% of Loan Payment Difficulties for NAME_CONTRACT_STATUS and NAME_CLIENT_TYPE',fontdict={'fontsize':18},pad=20)

plt.show()


#NAME_HOUSING_TYPE Vs NAME_CONTRACT_STATUS

plt.figure(figsize=[20,10])
sns.countplot(data=merged_df, y='NAME_HOUSING_TYPE', order=merged_df['NAME_HOUSING_TYPE'].value_counts().index,
             hue='NAME_CONTRACT_STATUS').set_title('NAME_HOUSING_TYPE Vs NAME_CONTRACT_STATUS',
                                               fontsize=30, color='blue',pad= 20)
plt.ylabel("NAME_HOUSING_TYPE",fontdict={'fontsize':25,'fontweight':5,'color':'Blue'})

plt.show()


#CODE_GENDER Vs NAME_CONTRACT_STATUS

plt.figure(figsize=[20,10])
sns.countplot(data=merged_df, y='CODE_GENDER', order=merged_df['CODE_GENDER'].value_counts().index,
             hue='NAME_CONTRACT_STATUS').set_title('CODE_GENDER Vs NAME_CONTRACT_STATUS',
                                               fontsize=30, color='blue',pad= 20)
plt.ylabel("CODE_GENDER",fontdict={'fontsize':25,'fontweight':5,'color':'Blue'})

plt.show()


#NAME_EDUCATION_TYPE Vs NAME_CONTRACT_STATUS

plt.figure(figsize=[20,10])

sns.countplot(data=merged_df, y='NAME_EDUCATION_TYPE', order=merged_df['NAME_EDUCATION_TYPE'].value_counts().index,
             hue='NAME_CONTRACT_STATUS',palette='crest').set_title('NAME_EDUCATION_TYPE Vs NAME_CONTRACT_STATUS',
                                               fontsize=30, color='blue',pad= 20)


plt.ylabel("NAME_EDUCATION_TYPE",fontdict={'fontsize':25,'fontweight':5,'color':'blue'})

plt.show()


#AMT_CREDIT_SLAB Vs NAME_CONTRACT_STATUS

plt.figure(figsize=[20,10])

sns.countplot(data=merged_df, y='AMT_CREDIT_slab', order=merged_df['AMT_CREDIT_slab'].value_counts().index,
             hue='NAME_CONTRACT_STATUS',palette='icefire').set_title('AMT_CREDIT_slab Vs NAME_CONTRACT_STATUS',
                                               fontsize=30, color='Blue',pad= 20)


plt.ylabel("AMT_CREDIT_slab",fontdict={'fontsize':25,'fontweight':5,'color':'Blue'})

plt.show()


#AGE_BINS Vs NAME_CONTRACT_STATUS

plt.figure(figsize=[20,10])

sns.countplot(data=merged_df, y='AGE_BINS', order=merged_df['AGE_BINS'].value_counts().index,
             hue='NAME_CONTRACT_STATUS',palette='cubehelix').set_title('AGE_BINS Vs NAME_CONTRACT_STATUS',
                                               fontsize=30, color='blue',pad= 20)


plt.ylabel("NAME_EDUCATION_TYPE",fontdict={'fontsize':25,'fontweight':5,'color':'blue'})

plt.show()


#INCOME_SLAB Vs NAME_CONTRACT_STATUS

plt.figure(figsize=[20,10])

sns.countplot(data=merged_df, y='INCOME_SLAB', order=merged_df['INCOME_SLAB'].value_counts().index,
             hue='NAME_CONTRACT_STATUS',palette='Paired').set_title('INCOME_SLAB Vs NAME_CONTRACT_STATUS',
                                               fontsize=30, color='Blue',pad= 20)


plt.ylabel("INCOME_SLAB",fontdict={'fontsize':25,'fontweight':5,'color':'Blue'})

plt.show()


#Contract type Vs AMT_CREDIT

plt.figure(figsize=[20,10])
sns.boxenplot(x=merged_df['NAME_CONTRACT_STATUS'], y=merged_df['AMT_CREDIT'], hue=merged_df['TARGET'],
             palette='viridis').set_title("Contract Vs AMT_CREDIT", fontsize=30,color='blue',pad=20)
plt.xlabel("NAME_CONTRACT_STATUS",fontdict={'fontsize':25,'fontweight':5,'color':'Blue'})
plt.ylabel("AMT_CREDIT",fontdict={'fontsize':25,'fontweight':5,'color':'Blue'})

plt.show()


#INCOME_SLAB Vs AMT_CREDIT

plt.figure(figsize=[20,10])
sns.boxenplot(x=merged_df['INCOME_SLAB'], y=merged_df['AMT_CREDIT'], hue=merged_df['TARGET'],
             palette='viridis').set_title("INCOME_SLAB Vs AMT_CREDIT", fontsize=30,color='blue',pad=20)
plt.xlabel("INCOME_SLAB",fontdict={'fontsize':25,'fontweight':5,'color':'Blue'})
plt.ylabel("AMT_CREDIT",fontdict={'fontsize':25,'fontweight':5,'color':'Blue'})

plt.show()


#NAME_INCOME_TYPE Vs AMT_CREDIT

plt.figure(figsize=[20,10])
sns.boxenplot(x=merged_df['NAME_INCOME_TYPE'], y=merged_df['AMT_CREDIT'], hue=merged_df['TARGET'],
             palette='flare').set_title("NAME_INCOME_TYPE Vs AMT_CREDIT", fontsize=30,color='blue',pad=20)
plt.xlabel("NAME_INCOME_TYPE",fontdict={'fontsize':25,'fontweight':5,'color':'Blue'})
plt.ylabel("AMT_CREDIT",fontdict={'fontsize':25,'fontweight':5,'color':'Blue'})

plt.show()


#NAME_CLIENT_TYPE Vs AMT_CREDIT

plt.figure(figsize=[20,10])
sns.boxenplot(x=merged_df['NAME_CLIENT_TYPE'], y=merged_df['AMT_CREDIT'], hue=merged_df['TARGET'],
             palette='light:#5A9').set_title("NAME_CLIENT_TYPE Vs AMT_CREDIT", fontsize=30,color='blue',pad=20)
plt.xlabel("NAME_CLIENT_TYPE",fontdict={'fontsize':25,'fontweight':5,'color':'Blue'})
plt.ylabel("AMT_CREDIT",fontdict={'fontsize':25,'fontweight':5,'color':'Blue'})

plt.show()

	SK_ID_PREV	SK_ID_CURR	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	HOUR_APPR_PROCESS_START	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	RATE_INTEREST_PRIMARY	RATE_INTEREST_PRIVILEGED	DAYS_DECISION	SELLERPLACE_AREA	CNT_PAYMENT	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
count	1.670214e+06	1.670214e+06	1.297979e+06	1.670214e+06	1.670213e+06	7.743700e+05	1.284699e+06	1.670214e+06	1.670214e+06	774370.000000	5951.000000	5951.000000	1.670214e+06	1.670214e+06	1.297984e+06	997149.000000	997149.000000	997149.000000	997149.000000	997149.000000	997149.000000
mean	1.923089e+06	2.783572e+05	1.595512e+04	1.752339e+05	1.961140e+05	6.697402e+03	2.278473e+05	1.248418e+01	9.964675e-01	0.079637	0.188357	0.773503	-8.806797e+02	3.139511e+02	1.605408e+01	342209.855039	13826.269337	33767.774054	76582.403064	81992.343838	0.332570
std	5.325980e+05	1.028148e+05	1.478214e+04	2.927798e+05	3.185746e+05	2.092150e+04	3.153966e+05	3.334028e+00	5.932963e-02	0.107823	0.087671	0.100879	7.790997e+02	7.127443e+03	1.456729e+01	88916.115834	72444.869708	106857.034789	149647.415123	153303.516729	0.471134
min	1.000001e+06	1.000010e+05	0.000000e+00	0.000000e+00	0.000000e+00	-9.000000e-01	0.000000e+00	0.000000e+00	0.000000e+00	-0.000015	0.034781	0.373150	-2.922000e+03	-1.000000e+00	0.000000e+00	-2922.000000	-2892.000000	-2801.000000	-2889.000000	-2874.000000	0.000000
25%	1.461857e+06	1.893290e+05	6.321780e+03	1.872000e+04	2.416050e+04	0.000000e+00	5.084100e+04	1.000000e+01	1.000000e+00	0.000000	0.160716	0.715645	-1.300000e+03	-1.000000e+00	6.000000e+00	365243.000000	-1628.000000	-1242.000000	-1314.000000	-1270.000000	0.000000
50%	1.923110e+06	2.787145e+05	1.125000e+04	7.104600e+04	8.054100e+04	1.638000e+03	1.123200e+05	1.200000e+01	1.000000e+00	0.051605	0.189122	0.835095	-5.810000e+02	3.000000e+00	1.200000e+01	365243.000000	-831.000000	-361.000000	-537.000000	-499.000000	0.000000
75%	2.384280e+06	3.675140e+05	2.065842e+04	1.803600e+05	2.164185e+05	7.740000e+03	2.340000e+05	1.500000e+01	1.000000e+00	0.108909	0.193330	0.852537	-2.800000e+02	8.200000e+01	2.400000e+01	365243.000000	-411.000000	129.000000	-74.000000	-44.000000	1.000000
max	2.845382e+06	4.562550e+05	4.180581e+05	6.905160e+06	6.905160e+06	3.060045e+06	6.905160e+06	2.300000e+01	1.000000e+00	1.000000	1.000000	1.000000	-1.000000e+00	4.000000e+06	8.400000e+01	365243.000000	365243.000000	365243.000000	365243.000000	365243.000000	1.000000

	SK_ID_CURR	TARGET	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	OWN_CAR_AGE	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	FLAG_EMAIL	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	HOUR_APPR_PROCESS_START	REG_REGION_NOT_LIVE_REGION	REG_REGION_NOT_WORK_REGION	LIVE_REGION_NOT_WORK_REGION	REG_CITY_NOT_LIVE_CITY	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	EXT_SOURCE_1	EXT_SOURCE_2	EXT_SOURCE_3	APARTMENTS_AVG	BASEMENTAREA_AVG	YEARS_BEGINEXPLUATATION_AVG	YEARS_BUILD_AVG	COMMONAREA_AVG	ELEVATORS_AVG	ENTRANCES_AVG	FLOORSMAX_AVG	FLOORSMIN_AVG	LANDAREA_AVG	LIVINGAPARTMENTS_AVG	LIVINGAREA_AVG	NONLIVINGAPARTMENTS_AVG	NONLIVINGAREA_AVG	APARTMENTS_MODE	BASEMENTAREA_MODE	YEARS_BEGINEXPLUATATION_MODE	YEARS_BUILD_MODE	COMMONAREA_MODE	ELEVATORS_MODE	ENTRANCES_MODE	FLOORSMAX_MODE	FLOORSMIN_MODE	LANDAREA_MODE	LIVINGAPARTMENTS_MODE	LIVINGAREA_MODE	NONLIVINGAPARTMENTS_MODE	NONLIVINGAREA_MODE	APARTMENTS_MEDI	BASEMENTAREA_MEDI	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BUILD_MEDI	COMMONAREA_MEDI	ELEVATORS_MEDI	ENTRANCES_MEDI	FLOORSMAX_MEDI	FLOORSMIN_MEDI	LANDAREA_MEDI	LIVINGAPARTMENTS_MEDI	LIVINGAREA_MEDI	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAREA_MEDI	TOTALAREA_MODE	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_2	FLAG_DOCUMENT_3	FLAG_DOCUMENT_4	FLAG_DOCUMENT_5	FLAG_DOCUMENT_6	FLAG_DOCUMENT_7	FLAG_DOCUMENT_8	FLAG_DOCUMENT_9	FLAG_DOCUMENT_10	FLAG_DOCUMENT_11	FLAG_DOCUMENT_12	FLAG_DOCUMENT_13	FLAG_DOCUMENT_14	FLAG_DOCUMENT_15	FLAG_DOCUMENT_16	FLAG_DOCUMENT_17	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
count	307511.000000	307511.000000	307511.000000	3.075110e+05	3.075110e+05	307499.000000	3.072330e+05	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	104582.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307509.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	134133.000000	3.068510e+05	246546.000000	151450.00000	127568.000000	157504.000000	103023.000000	92646.000000	143620.000000	152683.000000	154491.000000	98869.000000	124921.000000	97312.000000	153161.000000	93997.000000	137829.000000	151450.000000	127568.000000	157504.000000	103023.000000	92646.000000	143620.000000	152683.000000	154491.000000	98869.000000	124921.000000	97312.000000	153161.000000	93997.000000	137829.000000	151450.000000	127568.000000	157504.000000	103023.000000	92646.000000	143620.000000	152683.000000	154491.000000	98869.000000	124921.000000	97312.000000	153161.000000	93997.000000	137829.000000	159080.000000	306490.000000	306490.000000	306490.000000	306490.000000	307510.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.00000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000
mean	278180.518577	0.080729	0.417052	1.687979e+05	5.990260e+05	27108.573909	5.383962e+05	0.020868	-16036.995067	63815.045904	-4986.120328	-2994.202373	12.061091	0.999997	0.819889	0.199368	0.998133	0.281066	0.056720	2.152665	2.052463	2.031521	12.063419	0.015144	0.050769	0.040659	0.078173	0.230454	0.179555	0.502130	5.143927e-01	0.510853	0.11744	0.088442	0.977735	0.752471	0.044621	0.078942	0.149725	0.226282	0.231894	0.066333	0.100775	0.107399	0.008809	0.028358	0.114231	0.087543	0.977065	0.759637	0.042553	0.074490	0.145193	0.222315	0.228058	0.064958	0.105645	0.105975	0.008076	0.027022	0.117850	0.087955	0.977752	0.755746	0.044595	0.078078	0.149213	0.225897	0.231625	0.067169	0.101954	0.108607	0.008651	0.028236	0.102547	1.422245	0.143421	1.405292	0.100049	-962.858788	0.000042	0.710023	0.000081	0.015115	0.088055	0.000192	0.081376	0.003896	0.000023	0.003912	0.000007	0.003525	0.002936	0.00121	0.009928	0.000267	0.008130	0.000595	0.000507	0.000335	0.006402	0.007000	0.034362	0.267395	0.265474	1.899974
std	102790.175348	0.272419	0.722121	2.371231e+05	4.024908e+05	14493.737315	3.694465e+05	0.013831	4363.988632	141275.766519	3522.886321	1509.450419	11.944812	0.001803	0.384280	0.399526	0.043164	0.449521	0.231307	0.910682	0.509034	0.502737	3.265832	0.122126	0.219526	0.197499	0.268444	0.421124	0.383817	0.211062	1.910602e-01	0.194844	0.10824	0.082438	0.059223	0.113280	0.076036	0.134576	0.100049	0.144641	0.161380	0.081184	0.092576	0.110565	0.047732	0.069523	0.107936	0.084307	0.064575	0.110111	0.074445	0.132256	0.100977	0.143709	0.161160	0.081750	0.097880	0.111845	0.046276	0.070254	0.109076	0.082179	0.059897	0.112066	0.076144	0.134467	0.100368	0.145067	0.161934	0.082167	0.093642	0.112260	0.047415	0.070166	0.107462	2.400989	0.446698	2.379803	0.362291	826.808487	0.006502	0.453752	0.009016	0.122010	0.283376	0.013850	0.273412	0.062295	0.004771	0.062424	0.002550	0.059268	0.054110	0.03476	0.099144	0.016327	0.089798	0.024387	0.022518	0.018299	0.083849	0.110757	0.204685	0.916002	0.794056	1.869295
min	100002.000000	0.000000	0.000000	2.565000e+04	4.500000e+04	1615.500000	4.050000e+04	0.000290	-25229.000000	-17912.000000	-24672.000000	-7197.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.014568	8.173617e-08	0.000527	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-4292.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	189145.500000	0.000000	0.000000	1.125000e+05	2.700000e+05	16524.000000	2.385000e+05	0.010006	-19682.000000	-2760.000000	-7479.500000	-4299.000000	5.000000	1.000000	1.000000	0.000000	1.000000	0.000000	0.000000	2.000000	2.000000	2.000000	10.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.334007	3.924574e-01	0.370650	0.05770	0.044200	0.976700	0.687200	0.007800	0.000000	0.069000	0.166700	0.083300	0.018700	0.050400	0.045300	0.000000	0.000000	0.052500	0.040700	0.976700	0.699400	0.007200	0.000000	0.069000	0.166700	0.083300	0.016600	0.054200	0.042700	0.000000	0.000000	0.058300	0.043700	0.976700	0.691400	0.007900	0.000000	0.069000	0.166700	0.083300	0.018700	0.051300	0.045700	0.000000	0.000000	0.041200	0.000000	0.000000	0.000000	0.000000	-1570.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	278202.000000	0.000000	0.000000	1.471500e+05	5.135310e+05	24903.000000	4.500000e+05	0.018850	-15750.000000	-1213.000000	-4504.000000	-3254.000000	9.000000	1.000000	1.000000	0.000000	1.000000	0.000000	0.000000	2.000000	2.000000	2.000000	12.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.505998	5.659614e-01	0.535276	0.08760	0.076300	0.981600	0.755200	0.021100	0.000000	0.137900	0.166700	0.208300	0.048100	0.075600	0.074500	0.000000	0.003600	0.084000	0.074600	0.981600	0.764800	0.019000	0.000000	0.137900	0.166700	0.208300	0.045800	0.077100	0.073100	0.000000	0.001100	0.086400	0.075800	0.981600	0.758500	0.020800	0.000000	0.137900	0.166700	0.208300	0.048700	0.076100	0.074900	0.000000	0.003100	0.068800	0.000000	0.000000	0.000000	0.000000	-757.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
75%	367142.500000	0.000000	1.000000	2.025000e+05	8.086500e+05	34596.000000	6.795000e+05	0.028663	-12413.000000	-289.000000	-2010.000000	-1720.000000	15.000000	1.000000	1.000000	0.000000	1.000000	1.000000	0.000000	3.000000	2.000000	2.000000	14.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.675053	6.636171e-01	0.669057	0.14850	0.112200	0.986600	0.823200	0.051500	0.120000	0.206900	0.333300	0.375000	0.085600	0.121000	0.129900	0.003900	0.027700	0.143900	0.112400	0.986600	0.823600	0.049000	0.120800	0.206900	0.333300	0.375000	0.084100	0.131300	0.125200	0.003900	0.023100	0.148900	0.111600	0.986600	0.825600	0.051300	0.120000	0.206900	0.333300	0.375000	0.086800	0.123100	0.130300	0.003900	0.026600	0.127600	2.000000	0.000000	2.000000	0.000000	-274.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000
max	456255.000000	1.000000	19.000000	1.170000e+08	4.050000e+06	258025.500000	4.050000e+06	0.072508	-7489.000000	365243.000000	0.000000	0.000000	91.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	20.000000	3.000000	3.000000	23.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.962693	8.549997e-01	0.896010	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	348.000000	34.000000	344.000000	24.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	4.000000	9.000000	8.000000	27.000000	261.000000	25.000000

NAME_CONTRACT_STATUS	Approved	Canceled	Refused	Unused offer
NAME_CLIENT_TYPE
New	0.088216	0.145205	0.110940	0.089448
Refreshed	0.065158	0.081098	0.117412	0.074324
Repeater	0.072144	0.091767	0.120596	0.083338
XNA	0.071264	0.101377	0.135714	0.103448

NAME_CONTRACT_STATUS	Approved	Canceled	Refused	Unused offer
NAME_CONTRACT_TYPE
Cash loans	0.078105	0.094178	0.123735	0.084637
Revolving loans	0.049836	0.058751	0.069429	0.061972

	SK_ID_PREV	SK_ID_CURR	NAME_CONTRACT_TYPE	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	FLAG_LAST_APPL_PER_CONTRACT	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	RATE_INTEREST_PRIMARY	RATE_INTEREST_PRIVILEGED	NAME_CASH_LOAN_PURPOSE	NAME_CONTRACT_STATUS	DAYS_DECISION	NAME_PAYMENT_TYPE	CODE_REJECT_REASON	NAME_TYPE_SUITE	NAME_CLIENT_TYPE	NAME_GOODS_CATEGORY	NAME_PORTFOLIO	NAME_PRODUCT_TYPE	CHANNEL_TYPE	SELLERPLACE_AREA	NAME_SELLER_INDUSTRY	CNT_PAYMENT	NAME_YIELD_GROUP	PRODUCT_COMBINATION	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
count	1670214	1670214	1670214	1297979	1670214	1670213	774370	1284699	1670214	1670214	1670214	1670214	774370	5951	5951	1670214	1670214	1670214	1670214	1670214	849809	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1297984	1670214	1669868	997149	997149	997149	997149	997149	997149
size	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214	1670214
nunique	1670214	338857	4	357959	93885	86803	29278	93885	7	24	2	2	207033	148	25	25	4	2922	4	9	7	4	28	5	3	8	2097	11	49	5	17	2838	2892	4605	2873	2830	2

	SK_ID_CURR	TARGET	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	NAME_TYPE_SUITE	NAME_INCOME_TYPE	NAME_EDUCATION_TYPE	NAME_FAMILY_STATUS	NAME_HOUSING_TYPE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	OWN_CAR_AGE	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	FLAG_EMAIL	OCCUPATION_TYPE	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	REG_REGION_NOT_LIVE_REGION	REG_REGION_NOT_WORK_REGION	LIVE_REGION_NOT_WORK_REGION	REG_CITY_NOT_LIVE_CITY	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	ORGANIZATION_TYPE	EXT_SOURCE_1	EXT_SOURCE_2	EXT_SOURCE_3	APARTMENTS_AVG	BASEMENTAREA_AVG	YEARS_BEGINEXPLUATATION_AVG	YEARS_BUILD_AVG	COMMONAREA_AVG	ELEVATORS_AVG	ENTRANCES_AVG	FLOORSMAX_AVG	FLOORSMIN_AVG	LANDAREA_AVG	LIVINGAPARTMENTS_AVG	LIVINGAREA_AVG	NONLIVINGAPARTMENTS_AVG	NONLIVINGAREA_AVG	APARTMENTS_MODE	BASEMENTAREA_MODE	YEARS_BEGINEXPLUATATION_MODE	YEARS_BUILD_MODE	COMMONAREA_MODE	ELEVATORS_MODE	ENTRANCES_MODE	FLOORSMAX_MODE	FLOORSMIN_MODE	LANDAREA_MODE	LIVINGAPARTMENTS_MODE	LIVINGAREA_MODE	NONLIVINGAPARTMENTS_MODE	NONLIVINGAREA_MODE	APARTMENTS_MEDI	BASEMENTAREA_MEDI	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BUILD_MEDI	COMMONAREA_MEDI	ELEVATORS_MEDI	ENTRANCES_MEDI	FLOORSMAX_MEDI	FLOORSMIN_MEDI	LANDAREA_MEDI	LIVINGAPARTMENTS_MEDI	LIVINGAREA_MEDI	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAREA_MEDI	FONDKAPREMONT_MODE	HOUSETYPE_MODE	TOTALAREA_MODE	WALLSMATERIAL_MODE	EMERGENCYSTATE_MODE	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_2	FLAG_DOCUMENT_3	FLAG_DOCUMENT_4	FLAG_DOCUMENT_5	FLAG_DOCUMENT_6	FLAG_DOCUMENT_7	FLAG_DOCUMENT_8	FLAG_DOCUMENT_9	FLAG_DOCUMENT_10	FLAG_DOCUMENT_11	FLAG_DOCUMENT_12	FLAG_DOCUMENT_13	FLAG_DOCUMENT_14	FLAG_DOCUMENT_15	FLAG_DOCUMENT_16	FLAG_DOCUMENT_17	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
count	307511	307511	307511	307511	307511	307511	307511	307511	307511	307499	307233	306219	307511	307511	307511	307511	307511	307511	307511	307511	307511	104582	307511	307511	307511	307511	307511	307511	211120	307509	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	134133	306851	246546	151450	127568	157504	103023	92646	143620	152683	154491	98869	124921	97312	153161	93997	137829	151450	127568	157504	103023	92646	143620	152683	154491	98869	124921	97312	153161	93997	137829	151450	127568	157504	103023	92646	143620	152683	154491	98869	124921	97312	153161	93997	137829	97216	153214	159080	151170	161756	306490	306490	306490	306490	307510	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	265992	265992	265992	265992	265992	265992
size	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511	307511
nunique	307511	2	2	3	2	2	15	2548	5603	13672	1002	7	8	5	6	6	81	17460	12574	15688	6168	62	2	2	2	2	2	2	18	17	3	3	7	24	2	2	2	2	2	2	58	114584	119831	814	2339	3780	285	149	3181	257	285	403	305	3527	1868	5199	386	3290	760	3841	221	154	3128	26	30	25	25	3563	736	5301	167	3327	1148	3772	245	151	3202	46	46	49	47	3560	1097	5281	214	3323	4	3	5116	7	2	33	10	33	9	3773	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	5	9	9	24	11	25

	SK_ID_PREV	SK_ID_CURR	NAME_CONTRACT_TYPE	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	FLAG_LAST_APPL_PER_CONTRACT	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	RATE_INTEREST_PRIMARY	RATE_INTEREST_PRIVILEGED	NAME_CASH_LOAN_PURPOSE	NAME_CONTRACT_STATUS	DAYS_DECISION	NAME_PAYMENT_TYPE	CODE_REJECT_REASON	NAME_TYPE_SUITE	NAME_CLIENT_TYPE	NAME_GOODS_CATEGORY	NAME_PORTFOLIO	NAME_PRODUCT_TYPE	CHANNEL_TYPE	SELLERPLACE_AREA	NAME_SELLER_INDUSTRY	CNT_PAYMENT	NAME_YIELD_GROUP	PRODUCT_COMBINATION	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
0	2030495	271877	Consumer loans	1730.430	17145.0	17145.0	0.0	17145.0	SATURDAY	15	Y	1	0.0	0.182832	0.867336	XAP	Approved	-73	Cash through the bank	XAP	NaN	Repeater	Mobile	POS	XNA	Country-wide	35	Connectivity	12.0	middle	POS mobile with interest	365243.0	-42.0	300.0	-42.0	-37.0	0.0
1	2802425	108129	Cash loans	25188.615	607500.0	679671.0	NaN	607500.0	THURSDAY	11	Y	1	NaN	NaN	NaN	XNA	Approved	-164	XNA	XAP	Unaccompanied	Repeater	XNA	Cash	x-sell	Contact center	-1	XNA	36.0	low_action	Cash X-Sell: low	365243.0	-134.0	916.0	365243.0	365243.0	1.0
2	2523466	122040	Cash loans	15060.735	112500.0	136444.5	NaN	112500.0	TUESDAY	11	Y	1	NaN	NaN	NaN	XNA	Approved	-301	Cash through the bank	XAP	Spouse, partner	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	12.0	high	Cash X-Sell: high	365243.0	-271.0	59.0	365243.0	365243.0	1.0
3	2819243	176158	Cash loans	47041.335	450000.0	470790.0	NaN	450000.0	MONDAY	7	Y	1	NaN	NaN	NaN	XNA	Approved	-512	Cash through the bank	XAP	NaN	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	12.0	middle	Cash X-Sell: middle	365243.0	-482.0	-152.0	-182.0	-177.0	1.0
4	1784265	202054	Cash loans	31924.395	337500.0	404055.0	NaN	337500.0	THURSDAY	9	Y	1	NaN	NaN	NaN	Repairs	Refused	-781	Cash through the bank	HC	NaN	Repeater	XNA	Cash	walk-in	Credit and cash offices	-1	XNA	24.0	high	Cash Street: high	NaN	NaN	NaN	NaN	NaN	NaN

CAHNGING ALL VALUES OF COLUMNS WITH DATA TYPE OBJECT TO STRING¶